From 4c6099f8b17d1156d63310748dbb58274107b238 Mon Sep 17 00:00:00 2001
From: victor-navarro <navarrov@cardiff.ac.uk>
Date: Sat, 2 Mar 2024 10:46:11 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20victor-n?=
 =?UTF-8?q?avarro/calmr@9a379307cce643147c43cab891ebe50e6bbe9f2f=20?=
 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 articles/calmr_fits.html                      |  24 +++++-----
 articles/parallelism_in_calmr.html            |   4 +-
 articles/using_time_models.html               |  42 +++++++++---------
 .../figure-html/unnamed-chunk-6-1.png         | Bin 227579 -> 214085 bytes
 .../figure-html/unnamed-chunk-6-2.png         | Bin 251500 -> 235027 bytes
 .../figure-html/unnamed-chunk-6-3.png         | Bin 233774 -> 243465 bytes
 pkgdown.yml                                   |   2 +-
 reference/RSA.html                            |   8 ++--
 search.json                                   |   2 +-
 9 files changed, 41 insertions(+), 41 deletions(-)

diff --git a/articles/calmr_fits.html b/articles/calmr_fits.html
index dc8c43b..eca8528 100644
--- a/articles/calmr_fits.html
+++ b/articles/calmr_fits.html
@@ -242,18 +242,18 @@ <h3 id="writing-the-model-function">Writing the model function<a class="anchor"
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $acts</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 1,272 × 10</span></span></span>
-<span><span class="co">#&gt;    model  group phase    trial_type trial s1      s2      block_size type  value</span></span>
-<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> HD2022 CB1   training L&gt;(Pellet)     1 LPellet L                2 comb…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> HD2022 CB1   training L&gt;(Pellet)     1 LPellet Pellet           2 comb…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> HD2022 CB1   training L&gt;(Pellet)     1 LPellet R                2 comb…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> HD2022 CB1   training L&gt;(Pellet)     1 LPellet Sucrose          2 comb…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> HD2022 CB1   training L&gt;(Pellet)     1 L       L                2 chai…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> HD2022 CB1   training L&gt;(Pellet)     1 L       Pellet           2 chai…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> HD2022 CB1   training L&gt;(Pellet)     1 L       R                2 chai…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> HD2022 CB1   training L&gt;(Pellet)     1 L       Sucrose          2 chai…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> HD2022 CB1   training L&gt;(Pellet)     1 Pellet  L                2 chai…     0</span></span>
-<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> HD2022 CB1   training L&gt;(Pellet)     1 Pellet  Pellet           2 chai…     0</span></span>
+<span><span class="co">#&gt;    model  group phase    trial_type  trial s1      s2     block_size type  value</span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>       <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> HD2022 CB1   training L&gt;(Pellet)      1 LPellet L               2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> HD2022 CB1   training L&gt;(Pellet)      1 LPellet Pellet          2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> HD2022 CB1   training L&gt;(Pellet)      1 LPellet R               2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> HD2022 CB1   training L&gt;(Pellet)      1 LPellet Sucro…          2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> HD2022 CB1   training R&gt;(Sucrose)     2 LPellet L               2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> HD2022 CB1   training R&gt;(Sucrose)     2 LPellet Pellet          2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> HD2022 CB1   training R&gt;(Sucrose)     2 LPellet R               2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> HD2022 CB1   training R&gt;(Sucrose)     2 LPellet Sucro…          2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> HD2022 CB1   training R&gt;(Sucrose)     3 LPellet L               2 comb…     0</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> HD2022 CB1   training R&gt;(Sucrose)     3 LPellet Pellet          2 comb…     0</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,262 more rows</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $rs</span></span>
diff --git a/articles/parallelism_in_calmr.html b/articles/parallelism_in_calmr.html
index 3dcb1f9..98061be 100644
--- a/articles/parallelism_in_calmr.html
+++ b/articles/parallelism_in_calmr.html
@@ -145,7 +145,7 @@ <h3 id="why-run-things-in-parallel">Why run things in parallel?<a class="anchor"
 <span><span class="va">end</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/proc.time.html" class="external-link">proc.time</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="va">start</span></span>
 <span><span class="va">end</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   5.243   0.064   3.781</span></span></code></pre></div>
+<span><span class="co">#&gt;   5.496   0.087   3.954</span></span></code></pre></div>
 <p>You can see the timings above, under the <code>elapsed</code> column.
 Let’s try parallelizing now.</p>
 </div>
@@ -167,7 +167,7 @@ <h3 id="running-an-experiment-in-parallel">Running an experiment in parallel<a c
 <span><span class="va">end</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/proc.time.html" class="external-link">proc.time</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="va">start</span></span>
 <span><span class="va">end</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.625   0.095   4.039</span></span>
+<span><span class="co">#&gt;   0.633   0.075   4.155</span></span>
 <span></span>
 <span><span class="co"># go back to non-parallel evaluations</span></span>
 <span><span class="fu"><a href="https://future.futureverse.org/reference/plan.html" class="external-link">plan</a></span><span class="op">(</span><span class="va">sequential</span><span class="op">)</span></span></code></pre></div>
diff --git a/articles/using_time_models.html b/articles/using_time_models.html
index 5f3a541..9b215ef 100644
--- a/articles/using_time_models.html
+++ b/articles/using_time_models.html
@@ -254,27 +254,27 @@ <h4 id="specifying-a-design-for-time-based-models">Specifying a design for time-
 <span>  model <span class="op">=</span> <span class="st">"ANCCR"</span></span>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu"><a href="../reference/CalmrExperiment-methods.html">experience</a></span><span class="op">(</span><span class="va">experiment</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, <span class="fl">20</span><span class="op">)</span></span>
-<span><span class="co">#&gt;    group trial tp     tn is_test  phase block_size stimulus      time reward_mag</span></span>
-<span><span class="co">#&gt; 1     FN     1  4 T&gt;(US)   FALSE phase1          2        T  19.13618          1</span></span>
-<span><span class="co">#&gt; 2     FN     1  4 T&gt;(US)   FALSE phase1          2       US  20.13618          1</span></span>
-<span><span class="co">#&gt; 3     FN     2  3    F&gt;T   FALSE phase1          2        F  21.25171          1</span></span>
-<span><span class="co">#&gt; 4     FN     2  3    F&gt;T   FALSE phase1          2        T  22.25171          1</span></span>
-<span><span class="co">#&gt; 5     FN     3  4 T&gt;(US)   FALSE phase1          2        T  98.55150          1</span></span>
-<span><span class="co">#&gt; 6     FN     3  4 T&gt;(US)   FALSE phase1          2       US  99.55150          1</span></span>
-<span><span class="co">#&gt; 7     FN     4  3    F&gt;T   FALSE phase1          2        F 162.68908          1</span></span>
-<span><span class="co">#&gt; 8     FN     4  3    F&gt;T   FALSE phase1          2        T 163.68908          1</span></span>
-<span><span class="co">#&gt; 9     FN     5  3    F&gt;T   FALSE phase1          2        F 207.74943          1</span></span>
-<span><span class="co">#&gt; 10    FN     5  3    F&gt;T   FALSE phase1          2        T 208.74943          1</span></span>
-<span><span class="co">#&gt; 11    FN     6  4 T&gt;(US)   FALSE phase1          2        T 236.62214          1</span></span>
-<span><span class="co">#&gt; 12    FN     6  4 T&gt;(US)   FALSE phase1          2       US 237.62214          1</span></span>
-<span><span class="co">#&gt; 13    FN     7  4 T&gt;(US)   FALSE phase1          2        T 259.46315          1</span></span>
-<span><span class="co">#&gt; 14    FN     7  4 T&gt;(US)   FALSE phase1          2       US 260.46315          1</span></span>
-<span><span class="co">#&gt; 15    FN     8  3    F&gt;T   FALSE phase1          2        F 272.24472          1</span></span>
-<span><span class="co">#&gt; 16    FN     8  3    F&gt;T   FALSE phase1          2        T 273.24472          1</span></span>
-<span><span class="co">#&gt; 17    FN     9  3    F&gt;T   FALSE phase1          2        F 337.99508          1</span></span>
-<span><span class="co">#&gt; 18    FN     9  3    F&gt;T   FALSE phase1          2        T 338.99508          1</span></span>
-<span><span class="co">#&gt; 19    FN    10  4 T&gt;(US)   FALSE phase1          2        T 360.41439          1</span></span>
-<span><span class="co">#&gt; 20    FN    10  4 T&gt;(US)   FALSE phase1          2       US 361.41439          1</span></span></code></pre></div>
+<span><span class="co">#&gt;    group trial tp     tn is_test  phase block_size stimulus     time reward_mag</span></span>
+<span><span class="co">#&gt; 1     FN     1  3    F&gt;T   FALSE phase1          2        F  90.0000          1</span></span>
+<span><span class="co">#&gt; 2     FN     1  3    F&gt;T   FALSE phase1          2        T  91.0000          1</span></span>
+<span><span class="co">#&gt; 3     FN     2  4 T&gt;(US)   FALSE phase1          2        T 104.3101          1</span></span>
+<span><span class="co">#&gt; 4     FN     2  4 T&gt;(US)   FALSE phase1          2       US 105.3101          1</span></span>
+<span><span class="co">#&gt; 5     FN     3  3    F&gt;T   FALSE phase1          2        F 115.9003          1</span></span>
+<span><span class="co">#&gt; 6     FN     3  3    F&gt;T   FALSE phase1          2        T 116.9003          1</span></span>
+<span><span class="co">#&gt; 7     FN     4  4 T&gt;(US)   FALSE phase1          2        T 130.3224          1</span></span>
+<span><span class="co">#&gt; 8     FN     4  4 T&gt;(US)   FALSE phase1          2       US 131.3224          1</span></span>
+<span><span class="co">#&gt; 9     FN     5  3    F&gt;T   FALSE phase1          2        F 140.0717          1</span></span>
+<span><span class="co">#&gt; 10    FN     5  3    F&gt;T   FALSE phase1          2        T 141.0717          1</span></span>
+<span><span class="co">#&gt; 11    FN     6  4 T&gt;(US)   FALSE phase1          2        T 144.2789          1</span></span>
+<span><span class="co">#&gt; 12    FN     6  4 T&gt;(US)   FALSE phase1          2       US 145.2789          1</span></span>
+<span><span class="co">#&gt; 13    FN     7  4 T&gt;(US)   FALSE phase1          2        T 159.1877          1</span></span>
+<span><span class="co">#&gt; 14    FN     7  4 T&gt;(US)   FALSE phase1          2       US 160.1877          1</span></span>
+<span><span class="co">#&gt; 15    FN     8  3    F&gt;T   FALSE phase1          2        F 164.9881          1</span></span>
+<span><span class="co">#&gt; 16    FN     8  3    F&gt;T   FALSE phase1          2        T 165.9881          1</span></span>
+<span><span class="co">#&gt; 17    FN     9  4 T&gt;(US)   FALSE phase1          2        T 173.0267          1</span></span>
+<span><span class="co">#&gt; 18    FN     9  4 T&gt;(US)   FALSE phase1          2       US 174.0267          1</span></span>
+<span><span class="co">#&gt; 19    FN    10  3    F&gt;T   FALSE phase1          2        F 185.9990          1</span></span>
+<span><span class="co">#&gt; 20    FN    10  3    F&gt;T   FALSE phase1          2        T 186.9990          1</span></span></code></pre></div>
 <p>As you can see above, there are several rows per trial, each
 specifying a different stimulus. Time-based models like ANCCR run over a
 time log because they make ample use of the time difference between
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diff --git a/pkgdown.yml b/pkgdown.yml
index 32789f6..e4ec165 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -15,7 +15,7 @@ articles:
   heidi_similarity: heidi_similarity.html
   parallelism_in_calmr: parallelism_in_calmr.html
   using_time_models: using_time_models.html
-last_built: 2024-03-01T15:38Z
+last_built: 2024-03-02T10:45Z
 urls:
   reference: https://victornavarro.org/calmr/reference
   article: https://victornavarro.org/calmr/articles
diff --git a/reference/RSA.html b/reference/RSA.html
index a5118fc..08ae296 100644
--- a/reference/RSA.html
+++ b/reference/RSA.html
@@ -160,10 +160,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> CalmrRSA object</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ---------------</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Correlation matrix:</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>              HD2022.vs RW1972.vs PKH1982.eivs</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> HD2022.vs    1.0000000 1.0000000    0.9326953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> RW1972.vs    1.0000000 1.0000000    0.9326953</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> PKH1982.eivs 0.9326953 0.9326953    1.0000000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               HD2022.vs  RW1972.vs PKH1982.eivs</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> HD2022.vs     1.0000000  1.0000000   -0.9178532</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> RW1972.vs     1.0000000  1.0000000   -0.9178532</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> PKH1982.eivs -0.9178532 -0.9178532    1.0000000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Significance matrix:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
diff --git a/search.json b/search.json
index 131cb48..95423f9 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://victornavarro.org/calmr/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://victornavarro.org/calmr/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://victornavarro.org/calmr/articles/ANCCR.html","id":"the-mathematics-behind-anccr","dir":"Articles","previous_headings":"","what":"The mathematics behind ANCCR","title":"ANCCR","text":"TODO","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"the-mathematics-behind-heidi","dir":"Articles","previous_headings":"","what":"The mathematics behind HeiDI","title":"HD2022","text":"HeiDI model four major components: 1) acquisition reciprocal associations stimuli, 2) pooling associations stimulus activations, 3) distribution activations stimulus-specific response units, 4) generation responses.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"acquiring-reciprocal-associations","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"1 - Acquiring reciprocal associations","title":"HD2022","text":"Whenever trial given, HeiDI learns associations among stimuli. association two stimuli, \\(\\) \\(j\\) denoted via \\(v_{,j}\\). association \\(v_{,j}\\) represents directional expectation: expectation \\(j\\) presented \\(\\). Furthermore, value represents nature effect \\(\\) representation \\(j\\). positive, presentation \\(\\) “excites” representation \\(j\\). negative, presentation \\(\\) “inhibits” representation \\(j\\). HeiDI learns “forward” associations stimuli, also reciprocal, “backward” associations. Thus, organisms presented \\(\\rightarrow j\\), organisms learn \\(v_{,j}\\), also \\(v_{j, }\\), expectation receiving \\(\\) presented \\(j\\). Note , sake brevity, learning equations specified forward associations.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"the-stimulus-expectation-rule","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 1 - Acquiring reciprocal associations","what":"1.1 - The stimulus expectation rule","title":"HD2022","text":"HeiDI generates expectations stimuli. expectation stimulus \\(j\\) (\\(e_j\\)) expressed \\[ \\tag{Eq. 1} e_j  = \\sum_{k}^{K}x_kv_{k,j} \\] \\(K\\) set containing stimuli experiment, \\(x_k\\) quantity denoting presence absence stimulus \\(k\\) (1 0, respectively)1.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"learning-rule","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 1 - Acquiring reciprocal associations","what":"1.2 - Learning rule","title":"HD2022","text":"HeiDI learns appropriate expectations via error-correction mechanisms. trial \\(t\\), association stimuli \\(\\) \\(j\\) expressed \\[ \\tag{Eq. 2} v_{,j, t} = v_{,j, t-1} + \\Delta v_{,j, t} \\] \\(v_{j,, t-1}\\) forward association \\(\\) \\(j\\) trial \\(t-1\\), \\(\\Delta v_{,j, t}\\) change association result trial \\(t\\). delta term uses pooled error term expressed \\[ \\tag{Eq. 3} \\Delta v_{,j} = x_i\\alpha_i(x_jc\\alpha_j - e_j) \\] \\(\\alpha_i\\) \\(\\alpha_j\\) parameters representing salience stimuli \\(\\) \\(j\\), respectively (\\(0 \\le \\alpha \\le 1\\)), \\(c\\) scaling constant (\\(c = 1\\)). Note term denoting trial, \\(t\\) omitted simplicity.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"pooling-the-strength-of-associations","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"2 - Pooling the strength of associations","title":"HD2022","text":"HeiDI pools stimulus associations activate stimulus-specific representations. activation representation stimulus \\(j\\), \\(a_j\\), defined : \\[ \\tag{Eq. 4} a_{j,M} = o_{j,M} + h_{j,M} \\] \\(o_{j,M}\\) denotes combined associative strength towards stimulus \\(j\\) presence stimuli \\(M\\), \\(h_{j,M}\\) denotes chained associative strength towards stimulus \\(j\\) presence stimuli \\(M\\).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"combined-associative-strength","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 2 - Pooling the strength of associations","what":"2.1 - Combined associative strength","title":"HD2022","text":"quantity \\(o_{j,M}\\) result combining associative strength forwards backwards associations stimulus \\(j\\) \\[ \\tag{Eq. 5} o_{j,M} = \\sum_{m \\neq j}^{M}v_{m,j} + \\left(\\frac{\\sum_{m \\neq j}^{M}v_{m,j} \\sum_{m \\neq j}^{M}v_{j,m}}{c}\\right) \\] sums run stimuli \\(M\\) presented trial, also different stimulus \\(j\\).2 left hand term describes forward associations stimuli \\(M\\) \\(j\\) affect representation \\(j\\), whereas right hand term describes backward associations \\(j\\) stimuli \\(M\\) affect representation (although modulated forward associations ).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"chained-associative-strength","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 2 - Pooling the strength of associations","what":"2.2 - Chained associative strength","title":"HD2022","text":"quantity \\(h_{j,M}\\) captures indirect associative strength stimuli \\(M\\) \\(j\\), via absent stimuli. , \\(h_{j,M}\\) defined \\[ \\tag{Eq. 6a} h_{j,M} = \\sum_{m \\neq j}^{M} \\sum_{n}^{N}\\frac{v_{m,n}o_{j,n}}{c} \\] N stimuli presented trial (.e., K-M). Note re-use \\(o\\), quantity defined Eq. 5. equation allows absent stimuli \\(N\\) influence representation stimulus \\(j\\), long association present stimuli \\(M\\). Honey Dwyer (2022), authors specify similarity-based mechanism modulates effect associative chains according similarity salience nominal retrieved stimuli. , Eq. 6a expanded : \\[ \\tag{Eq. 6b} h_{j,M} = \\sum_{m \\neq j}^{M} \\sum_{n}^{N}S(\\alpha_{n}, \\alpha'_n)\\frac{v_{m,n}o_{j,n}}{c} \\] \\(S\\) similarity function takes nominal salience stimulus n, \\(\\alpha_n\\) (perceived \\(n\\) actually presented trial) retrieved salience, \\(\\alpha'_n\\) (perceived \\(n\\) retrieved via stimuli M, see ahead). function defined : \\[ \\tag{Eq. 7} S(\\alpha_n, \\alpha'_n) = \\frac{\\alpha_n}{\\alpha_n + |\\alpha_n-\\alpha'_n|} \\times \\frac{\\alpha'_n}{\\alpha'_n+ |\\alpha_n-\\alpha'_n|} \\] Notably, whenever one nominal salience given stimulus, \\(\\alpha_n\\) arithmetic mean among nominal values (see “heidi_similarity” vignette).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"distributing-strength-into-stimulus-specific-response-units","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"3 - Distributing strength into stimulus-specific response units","title":"HD2022","text":"HeiDI distributes pooled stimulus-specific strength among \\(K\\) stimuli, according relative salience. activation response unit \\(j\\), \\(R_j\\) expressed \\[ \\tag{Eq. 8} R_{j,k} = \\frac{\\theta(j)}{\\sum_{k}^{K}\\theta(k)}a_{k,M} \\] \\(j \\K\\). \\(K\\) can include present absent stimuli, \\(\\theta\\) function depends whether stimulus \\(k\\) absent (.e., \\(k \\N\\)) (.e., \\(k \\M\\)), : \\[ \\tag{Eq. 9} \\theta(k) = \\begin{cases}     \\left |\\sum_{m}^{M}\\left( v_{m,k}+\\sum_{n \\neq k}^{N}\\frac{v_{m,n}v_{n,k}}{c}\\right) \\right|,& \\text{} k \\N\\\\     \\alpha_k, & \\text{otherwise} \\end{cases} \\] Note quantity absent stimuli absolute, order prevent negative \\(\\theta\\) values due inhibitory associations3. Also note summation term used left-hand side expression absent stimulus. implies present stimuli \\(M\\) contribute salience stimulus \\(k\\). Finally, note right side expression present stimuli contribute via direct association \\(k\\), \\(v_{m,k}\\), also associative chains absent stimuli (c.f., Eq. 6a).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"4 - Generating responses","title":"HD2022","text":"Finally, HeiDI responds. response-generating mechanisms HeiDI currently underspecified. current version, HeiDI’s responses product activation stimulus-specific response units connection units specific motor units. , activation motor unit \\(q\\), \\(r_q\\), given \\[ \\tag{Eq. 10} r_q = R_jw_{j,q} \\] \\(w_{j,q}\\) weight representing association stimulus-specific unit \\(j\\) motor unit \\(q\\).","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"the-mathematics-behind-mac1975","dir":"Articles","previous_headings":"","what":"The mathematics behind MAC1975","title":"MAC1975","text":"grand departure global error term models RW1972 (Rescorla & Wagner, 1972), MAC1975 model (Mackintosh, 1975) uses local error terms, changes stimulus associability (\\(\\alpha\\)) via error comparison mechanism promotes learning uncertain stimuli:","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"generating-expectations","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"1 - Generating expectations","title":"MAC1975","text":"Let \\(v_{k,j}\\) denote associative strength stimulus \\(k\\) stimulus \\(j\\). given trial, expectation stimulus \\(j\\), \\(e_j\\), given : \\[ \\tag{Eq.1} e_j = \\sum_{k}^{K}x_k v_{k,j} \\] \\(x_k\\) denotes presence (1) absence (0) stimulus \\(k\\), set \\(K\\) represents stimuli design.","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"2 - Learning associations","title":"MAC1975","text":"Changes association stimulus \\(\\) \\(j\\), \\(v_{,j}\\), given : \\[ \\tag{Eq.2} \\Delta v_{,j} = x_i \\alpha_i \\beta_j (\\lambda_j - v_{,j}) \\] \\(\\alpha_i\\) associability (attention devoted ) stimulus \\(\\), \\(\\beta_j\\) learning rate parameter determined properties \\(j\\), \\(\\lambda_j\\) maximum association strength supported \\(j\\) (asymptote).","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"learning-to-attend","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"3 - Learning to attend","title":"MAC1975","text":"parameter \\(\\alpha_i\\) changes function learning, proportionally difference absolute errors conveyed \\(\\) predictors1, via: \\[ \\tag{Eq.3} \\Delta \\alpha_{} = x_i\\theta_i \\sum_{j}^{K}\\gamma_j(|\\lambda_j - \\sum_{k \\ne }^{K}v_{k,j}|-|\\lambda_j -  v_{,j}|) \\] \\(\\theta_i\\) attentional learning rate parameter stimulus \\(\\) (usually fixed across stimuli). Although Mackintosh (1975) extend model account predictive power within-compound associations, implementation model package . can sometimes result unexpected behaviour, , Eq. 3 includes extra parameter \\(\\gamma_j\\) (defaulting 1/K) denotes whether expectation stimulus \\(j\\) contributes attentional learning. , user can set parameters manually order reflect contribution different experimental stimuli. example, simple “AB>(US)” design, setting \\(\\gamma_{US}\\) = 1 \\(\\gamma_{} = \\gamma_{B} = 0\\) leads behaviour original model.","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"4 - Generating responses","title":"MAC1975","text":"specification response-generating mechanisms MAC1975. However, simplest response function can adopted identity function stimulus expectations. , responses reflecting nature \\(j\\), \\(r_j\\), given : \\[ \\tag{Eq.4} r_j = e_j \\]","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"the-mathematics-behind-pkh1982","dir":"Articles","previous_headings":"","what":"The mathematics behind PKH1982","title":"PKH1982","text":"Another departure global error term models RW1972 (Rescorla & Wagner, 1972), PKH1982 model (Pearce et al., 1982) use error term learning excitatory associations (inhibitory associations), ties stimulus associability (\\(\\alpha\\)) absolute global prediction error. note: implementation model closely follows technical note CAL-R group possible. Divergences noted.","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"generating-expectations","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"1 - Generating expectations","title":"PKH1982","text":"Let \\(v_{k,j}\\) denote excitatory strength stimulus \\(k\\) stimulus \\(j\\), \\(v_{k,\\overline j}\\) inhibitory strength stimulus \\(k\\) stimulus \\(j\\) (effectively, “j” representation). given trial, net expectation stimulus \\(j\\), \\(e_j\\), given : \\[ \\tag{Eq.1} e_j = \\sum_{k}^{K}x_k v_{k,j} - \\sum_{k}^{K}x_k v_{k,\\overline j} \\] \\(x_k\\) denotes presence (1) absence (0) stimulus \\(k\\), set \\(K\\) represents stimuli design.","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"2 - Learning associations","title":"PKH1982","text":"Changes excitatory inhibitory associations stimuli given : \\[ \\tag{Eq.2a} \\Delta v_{,j} = \\delta_jx_i \\beta_{ex,j} \\alpha_i \\lambda_j \\] \\[ \\tag{Eq.2b} \\Delta v_{,\\overline j} = x_i \\beta_{,j} \\alpha_i  |\\overline{\\lambda_j}| \\] \\(\\beta_{ex,j}\\) \\(\\beta_{,j}\\) represent learning rates excitatory inhibitory associations, respectively, determined stimulus \\(j\\), \\(\\alpha_i\\) associability stimulus \\(\\), respectively, \\(\\lambda_j\\) \\(\\overline {\\lambda_j}\\) excitatory asymptote overexpectation stimulus \\(j\\), respectively. Importantly, \\(\\delta_j\\) Eq.2a parameter equal 1 expectation stimulus \\(j\\), lower excitatory asymptote (.e., \\(e_j < \\lambda_j\\)), 0 . implies model stops strengthening \\(v_{,j}\\) expectation \\(j\\) higher excitatory assymptote. mentioned introductory note, PKH1982 model learn excitatory associations via correction error. However, model learn inhibitory associations via correction error, overexpectation term , \\(\\overline {\\lambda_j}\\) equal \\(min(\\lambda_j - e_j, 0)\\), \\(min\\) minimum function. implies \\(\\overline {\\lambda_j}\\) takes non-zero values expectation \\(j\\) higher intensity trial (\\(\\lambda_j\\)).","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"learning-to-attend","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"3 - Learning to attend","title":"PKH1982","text":"associability parameter \\(\\alpha_i\\) changes completely trial trial function learning (note lack \\(\\Delta\\) ), change equal difference absolute global error, via: \\[ \\tag{Eq.3} \\Delta \\alpha_{} = x_i \\sum_{j}^{K}\\gamma_j(|\\lambda_j - e_j|) \\] \\(\\gamma_j\\) denotes contribution prediction error based jth stimulus. regard, important note Pearce et al. (1982) extend model account predictive power within-compound associations, yet implementation model package . can sometimes result unexpected behaviour, , Eq. 3 includes extra parameter \\(\\gamma_j\\) (defaulting 1/K) denotes whether expectation stimulus \\(j\\) contributes attentional learning. , user can set parameters manually order reflect contribution different experimental stimuli. example, simple “AB>(US)” design, setting \\(\\gamma_{US}\\) = 1 \\(\\gamma_{} = \\gamma_{B} = 0\\) leads behaviour original model. PKH1982 model improves upon Pearce & Hall (1980) model adding extra parameter controls rate associability changes. qualify changes associability determined Eq.3 via \\(\\Delta\\alpha_{}^{n}\\) (meaning happened trial \\(n\\)), can quantify total associability stimulus \\(\\) trial \\(n\\) via: \\[ \\tag{Eq.4} \\alpha_{}^{n} = \\begin{cases}   (1-\\theta_i) \\alpha_{}^{n-1} + \\theta_i\\Delta\\alpha_{j}^n &\\text{, } x_i = 1\\\\   \\alpha_{}^{n} & \\text{, otherwise} \\end{cases} \\] \\(\\theta_i\\) parameter determining rate associability decays (via \\(1-\\theta_i\\)), rate increments attention occur. Note changes associability apply stimuli presented trial (.e., \\(x_i = 1\\)); attention absent stimuli remains unchanged.","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"4 - Generating responses","title":"PKH1982","text":"specification response-generating mechanisms PKH1982. However, simplest response function can adopted identity function stimulus expectations. , responses reflecting nature \\(j\\), \\(r_j\\), given : \\[ \\tag{Eq.5} r_j = e_j \\]","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/RAND.html","id":"the-mathematics-behind-rand","dir":"Articles","previous_headings":"","what":"The mathematics behind RAND","title":"RAND","text":"RAND model RW-based model associations randomized every trial. Therefore, model responds randomly. model meant comparisons .","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"the-mathematics-behind-rw1972","dir":"Articles","previous_headings":"","what":"The mathematics behind RW1972","title":"RW1972","text":"influential associative learning model, RW1972 (Rescorla & Wagner, 1972), learns global error posits changes stimulus associability.","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"generating-expectations","dir":"Articles","previous_headings":"The mathematics behind RW1972","what":"1 - Generating expectations","title":"RW1972","text":"Let \\(v_{k,j}\\) denote associative strength stimulus \\(k\\) stimulus \\(j\\). given trial, expectation stimulus \\(j\\), \\(e_j\\), given : \\[ \\tag{Eq.1} e_j = \\sum_{k}^{K}x_k v_{k,j} \\] \\(x_k\\) denotes presence (1) absence (0) stimulus \\(k\\), set \\(K\\) represents stimuli design.","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind RW1972","what":"2 - Learning associations","title":"RW1972","text":"Changes association stimulus \\(\\) \\(j\\), \\(v_{,j}\\), given : \\[ \\tag{Eq.2} \\Delta v_{,j} = \\alpha_i \\beta_j (\\lambda_j - e_j) \\] \\(\\alpha_i\\) associability stimulus \\(\\), \\(\\beta_j\\) learning rate parameter determined properties \\(j\\)1, \\(\\lambda_j\\) maximum association strength supported \\(j\\) (asymptote).","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind RW1972","what":"3 - Generating responses","title":"RW1972","text":"specification response-generating mechanisms RW1972. However, simplest response function can adopted identity function stimulus expectations. , responses reflecting nature \\(j\\), \\(r_j\\), given : \\[ \\tag{Eq.3} r_j = e_j \\]","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"the-mathematics-behind-socr","dir":"Articles","previous_headings":"","what":"The mathematics behind SOCR","title":"SM2007","text":"first formalization comparator hypothesis (Miller & Matzel, 1988), sometimes competing retrieval model (SOCR; Stout & Miller, 2007) learns local error responds function relative associative strength present retrieved stimuli.","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"1 - Learning associations","title":"SM2007","text":"SOCR model uses two different learning equations strengthening weakening associations. Whenever two stimuli contiguous, strengthening occurs. case, strengthening association stimulus \\(\\) \\(j\\) trial \\(t\\), \\(v_{,j}^t\\) given : \\[ \\tag{Eq.1a} \\Delta v_{,j}^t = x^t_i \\alpha_i \\alpha_j (\\lambda_j - v_{,j}^{t-1}) \\] \\(x^t_i\\) denotes presence (1) absence (0) stimulus \\(\\) trial \\(t\\). , SOCR model learns stimuli presented. parameters \\(\\alpha_i\\) \\(\\alpha_j\\) saliencies stimuli j, respectively, \\(\\lambda_j\\) maximum association strength supported \\(j\\) (asymptote). Whenever stimulus \\(\\) presented alone (.e., stimulus \\(j\\) absent), weakening association given : \\[ \\tag{Eq.1b} \\Delta v_{,j}^t = x_i \\alpha_i \\times -\\omega_j v_{,j}^{t-1} \\] \\(\\omega_j\\) determines weakening rate stimulus \\(j\\).1","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"activating-stimuli","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"2 - Activating stimuli","title":"SM2007","text":"SOCR posits competition stimuli presented /associatively retrieved. Dropping trial notation sake simplicity, degree stimulus \\(\\) activates stimulus \\(j\\), \\(act_{,j}\\), given : \\[ \\tag{Eq.2} act_{, j} = x_i v_{,j} + x_j\\rho_j\\alpha_j \\] \\(\\rho_j\\) (bound 0 +\\(\\infty\\)) determines much salience stimulus \\(j\\) contributes unconditioned activation. first-order activation values key quantities involved comparison processes.","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"generating-responses-and-comparison-processes","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"3 - Generating responses and comparison processes","title":"SM2007","text":"Stimulus \\(\\) generates j-oriented responding time retrieval function relative ability activate stimulus \\(j\\). relative ability expressed comparison process, given : \\[ \\tag{Eq.3} r^j_i = act_{,j} - \\Sigma_{k \\neq ,j} ^K \\gamma_k \\times o_{,k,j} \\times r^k_i \\times r^j_k \\] \\(r^j_i\\) relative activation stimulus \\(j\\) stimulus \\(\\), \\(K\\) set experimental stimuli including \\(\\) \\(j\\), \\(\\gamma_k\\) parameter determining degree stimulus \\(k\\), comparison stimulus, contributes comparison process (bound 0 1), \\(o_{,k,j}\\) operator switch determines whether \\(\\) \\(k\\) associations \\(j\\) engage facilitation competition. Finally, \\(r^k_i\\) relative activation stimulus \\(k\\) stimulus \\(\\), representing ability stimulus \\(\\) activate comparison, \\(r^j_k\\) relative activation stimulus \\(j\\) stimulus \\(k\\), representing ability comparison stimulus \\(k\\) activate stimulus \\(j\\).2 notably, last two quantities (\\(r^k_i\\) \\(r^j_k\\)) also determined corresponding instantiations Eq. 3. , involve comparison processes . number potential comparison processes technically infinite (comparison process can nest two extra comparison processes ), user must determine order model using extra global parameter (order). n-th order models (\\(n > 0\\)), model behave like extended comparator hypothesis (Denniston et al., 2001), implementing \\(n\\) comparison processes time relative activations calculated. order = 0, SM2007 behave like originally written, consider one comparison process. Indeed, n-th order models accomplished via recursion using 0-th order model stopping condition. condition reached, \\(r^k_i\\) \\(r^j_k\\) terms Eq. 3 become \\(act_{,k}\\) \\(act_{k,j}\\), respectively.","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"switching-between-facilitation-and-competition","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"4 - Switching between facilitation and competition","title":"SM2007","text":"operator switch Eq. 3, \\(o_{,k,j}\\), changes subjects learn discriminate directly (via \\(\\)) indirectly activated (via \\(k\\)) representations stimulus \\(j\\). change quantity depends value \\(v_{,j}\\), follows: \\[ \\tag{Eq.4} \\Delta o_{,k,j} = \\begin{cases}   \\tau_j\\alpha_iv_{,k}v_{k,j}(1-o_{,k,j})   &\\text{, } v_{,j} = 0\\\\   1-o_{,k,j} & \\text{, otherwise} \\end{cases} \\] negative values \\(o\\) indicate facilitation positive values \\(o\\) indicate competition. default value operator switches outset training set -1 default. parameter \\(\\tau_j\\) specifies learning rate operator switches related stimulus \\(j\\).","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"using-the-calmr-app","dir":"Articles","previous_headings":"","what":"Using the calmr app","title":"calmr_app","text":"beginner useR simply want simulate experimental design see model , might interested using calmr application. calmr application offers GUI allows simulate experiments without writing code. want use online app, can find : https://victor-navarro.shinyapps.io/calmr_app/. Alternatively, installed calmr package, can launch app via calmr::calmr_app(). rest tutorial assumes app open ready run. Let’s break GUI.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"design-table","dir":"Articles","previous_headings":"The GUI","what":"Design Table","title":"calmr_app","text":"design table specify experimental design run. Using Group- Group+, can remove add groups design. Using Phase- Phase+, can remove add phases design. Parse Design button used parse design, required step run simulation. later. P1 P2 columns specify phases experiment simulated left right. entry columns specifies trials given corresponding groups (G1 G2, case). entries must obey special syntax (see calmr_basics additional information). now, suffice say : number trials specified via digits left trial. example, 10A(US) specifies ten “(US)” trials. Stimuli (elements) specified letters. example, 10AB(US) specifies elements B US. Named stimuli specified within parentheses. example, (US) implies element named “US” instead compound containing elements “U” “S”. Multiple trials per phase separated via forward slash (/). Additionally, one can choose randomize trials within phase ticking boxes R1 R2 columns. important note whatever user sets interact “Create trial blocks” option Options tab sidebar (see ahead). ’s full breakdown combinations behaviour: Table checked preferences checked: Trials shuffled within blocks possible (based greater common factor). example, 2A/2B gets shuffled 2 blocks containing one one B trial, 2A/1B gets shuffled 1 block containign two trials one B trial. Table unchecked preferences checked: Trials deterministically intermixed within blocks possible. example, 2A/2B gets shuffled 2 blocks, resulting sequence ABAB. Table checked preferences unchecked: Trials shuffled completely random. Table unchecked preferences unchecked: Trials given order appearance. example, 2A/2B results AABB sequence. Go ahead parse design. new things appear GUI.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"parameters","dir":"Articles","previous_headings":"The GUI","what":"Parameters","title":"calmr_app","text":"parsing valid design. user can set parameters experiment (including stimulus-related global parameters). case, Rescorla-Wagner model 4 parameters per stimulus. default values fairly sensible, can modify parameter hand favorite spreadsheet software. parametrization model calmr can sometimes differ appears literature. following table contains links documentation pages model.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"results","dir":"Articles","previous_headings":"The GUI","what":"Results","title":"calmr_app","text":"new button appears parsing experiment. final click button run model populate “Results” “Association Graphs” portions app. Go ahead run experiment. , new button appear, allowing download results spreadsheet. calmr app, results shown visually. Clicking bar graph (one containing “Blocking - Response Strength …” ) show plots available. first portion plot’s name denotes group’s name. , plot shows strength associations among stimuli experiment across trials (blocks), faceted phases columns, origin stimuli rows. example, yellow lines denote strength B US. top column corresponds (look label right) middle column corresponds B. Go ahead explore available plots. usually self explanatory, consult documentation package case something unclear (specially using obscure models).","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"association-graphs","dir":"Articles","previous_headings":"The GUI","what":"Association Graphs","title":"calmr_app","text":"bottom portion app shows network graphs depicting strength associations model given trial, groups. Yellow denotes excitatory strength (.e., positive values), gray denotes neutral strength (.e., values close zero), purple (shown ) shows inhibitory strength (.e., negative values). Move “Trial” slider explore associations model change across experiment.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"other-bits","dir":"Articles","previous_headings":"The GUI","what":"Other bits","title":"calmr_app","text":"sections implement bulk functionalities calmr app. following sections describe additional options convenience functions found useful.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"options","dir":"Articles","previous_headings":"The GUI > Other bits","what":"Options","title":"calmr_app","text":"set number iterations run experiment (important model behaviour sensitive trial order effects), whether want create trial blocks. also set can choose plot common scale y-axis (active default).","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"a-final-message","dir":"Articles","previous_headings":"The GUI","what":"A final message","title":"calmr_app","text":"Hope enjoy app! find bugs, comments, like something implemented, feel free post message package’s github repository drop line navarrov [] cardiff.ac.uk.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"your-first-simulation","dir":"Articles","previous_headings":"","what":"Your first simulation","title":"calmr_basics","text":"perform first simulation need: data.frame specifiying experiment design, list parameters model ’ll using.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"the-design-data-frame","dir":"Articles","previous_headings":"","what":"The design data.frame","title":"calmr_basics","text":"use data.frame specify experimental design. Let’s specify blocking design. rules design data.frame: row represents group. first column contains group labels. remaining columns organized pairs (trials phase, whether randomize ) trials phase column specified using rigid notation. observations : Trials preceded number. number represents number times trial given phase. “10A>(US)” means “>(US)” trial given 10 times. presence absence unconditioned stimulus denoted traditional “+” “-” symbols. Instead, use parenthesis denote “complex” stimuli. can thought element complex name (.e., one character). , “(US)” specifies single element represent US. vein, multiple characters parentheses denote individual elements. example, “AB” implies presence two stimuli, B. “>” character used separator “expectations” “correction” steps within trial. “10A>(US)” means model generates expectation , learns co-occurrence US. “/” character used trial separator (imply randomization ). Thus, “1A/1B” specifies single “” trial single “B” trial given phase. Recall randomization trials within phase specified column (, R1, R2, R3). “#” character used denote probe trials. contrast real life, probe trials entail update model’s associations. , probe trials can used track development key associations, repercussion model learns normal training trials. want check whether trial string work simulator, can use phase_parser. function returns list lot information, let’s print fields.","code":"library(calmr)  my_blocking <- data.frame(   Group = c(\"Exp\", \"Control\"),   Phase1 = c(\"10A>(US)\", \"10C>(US)\"),   R1 = c(FALSE, FALSE),   Phase2 = c(\"10AB>(US)\", \"10AB>(US)\"),   R2 = c(FALSE, FALSE),   Test = c(\"1A#/1B#\", \"1A#/1B#\"),   R3 = c(FALSE, FALSE) ) my_blocking #>     Group   Phase1    R1    Phase2    R2    Test    R3 #> 1     Exp 10A>(US) FALSE 10AB>(US) FALSE 1A#/1B# FALSE #> 2 Control 10C>(US) FALSE 10AB>(US) FALSE 1A#/1B# FALSE # not specifying a number of AB trials. Bad practice! trial <- phase_parser(\"AB/10AC\") trial$general_info[c(\"trial_names\", \"trial_repeats\")] #> $trial_names #> [1] \"AB\" \"AC\" #>  #> $trial_repeats #> [1]  1 10 # considering a configural cue for elements AB trial <- phase_parser(\"10AB(AB)(US)\") trial$general_info[c(\"func2nomi\")] #> $func2nomi #>    A    B   AB   US  #>  \"A\"  \"B\" \"AB\" \"US\""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"the-parameters-list","dir":"Articles","previous_headings":"","what":"The parameters list","title":"calmr_basics","text":"Now need pick model parameters. get models currently supported calmr, can call supported_models(). picking model, can get default parameters design get_parameters.","code":"supported_models() #> [1] \"HDI2020\" \"HD2022\"  \"RW1972\"  \"MAC1975\" \"PKH1982\" \"SM2007\"  \"RAND\"    #> [8] \"ANCCR\" my_pars <- get_parameters(my_blocking, model = \"RW1972\") # Increasing the beta parameter for US presentations my_pars$betas_on[\"US\"] <- .6 my_pars #> $alphas #>   A   B   C  US  #> 0.4 0.4 0.4 0.4  #>  #> $betas_on #>   A   B   C  US  #> 0.4 0.4 0.4 0.6  #>  #> $betas_off #>   A   B   C  US  #> 0.4 0.4 0.4 0.4  #>  #> $lambdas #>  A  B  C US  #>  1  1  1  1"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"the-optional-additional-options","dir":"Articles","previous_headings":"The parameters list","what":"The (optional) additional options","title":"calmr_basics","text":"opt , can also pass list simulation options. Simulation options entirely optional, don’t pass just get warning. can get default one get_exp_opts, modify accordingly.","code":"my_opts <- get_exp_opts() # Increase number of simulations to average out randomization effects my_opts$iterations <- 5 my_opts #> $iterations #> [1] 5 #>  #> $miniblocks #> [1] TRUE"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"simulating","dir":"Articles","previous_headings":"The parameters list","what":"Simulating","title":"calmr_basics","text":", can run simulation using run_experiment function. run_experiment returns lot information. str function best friend, plenty documentation go . course, advanced R user able dig data straight away. However, package also includes methods get quick look results.","code":"my_experiment <- run_experiment(   my_blocking,   model = \"RW1972\",   parameters = my_pars,   options = my_opts, ) slotNames(my_experiment) #> [1] \"arguments\" \"design\"    \"results\" # access arguments (see how many iterations we ran?) my_experiment@arguments #> # A tibble: 10 × 6 #>    iteration model  group   experience    mapping           parameters       #>        <int> <chr>  <chr>   <list>        <list>            <list>           #>  1         1 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  2         1 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  3         2 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  4         2 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  5         3 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  6         3 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  7         4 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  8         4 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  9         5 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #> 10         5 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> # access results results(my_experiment) #> $rs #> # A tibble: 704 × 9 #>    model  group   phase  trial_type trial s1    s2    block_size value #>    <chr>  <chr>   <chr>  <chr>      <int> <chr> <chr>      <dbl> <dbl> #>  1 RW1972 Control Phase1 C>(US)         1 A     A              1     0 #>  2 RW1972 Control Phase1 C>(US)         1 A     B              1     0 #>  3 RW1972 Control Phase1 C>(US)         1 A     C              1     0 #>  4 RW1972 Control Phase1 C>(US)         1 A     US             1     0 #>  5 RW1972 Control Phase1 C>(US)         1 B     A              1     0 #>  6 RW1972 Control Phase1 C>(US)         1 B     B              1     0 #>  7 RW1972 Control Phase1 C>(US)         1 B     C              1     0 #>  8 RW1972 Control Phase1 C>(US)         1 B     US             1     0 #>  9 RW1972 Control Phase1 C>(US)         1 C     A              1     0 #> 10 RW1972 Control Phase1 C>(US)         1 C     B              1     0 #> # ℹ 694 more rows #>  #> $vs #> # A tibble: 704 × 9 #>    model  group   phase  trial_type trial s1    s2    block_size value #>    <chr>  <chr>   <chr>  <chr>      <int> <chr> <chr>      <dbl> <dbl> #>  1 RW1972 Control Phase1 C>(US)         1 A     A              1     0 #>  2 RW1972 Control Phase1 C>(US)         1 A     B              1     0 #>  3 RW1972 Control Phase1 C>(US)         1 A     C              1     0 #>  4 RW1972 Control Phase1 C>(US)         1 A     US             1     0 #>  5 RW1972 Control Phase1 C>(US)         1 B     A              1     0 #>  6 RW1972 Control Phase1 C>(US)         1 B     B              1     0 #>  7 RW1972 Control Phase1 C>(US)         1 B     C              1     0 #>  8 RW1972 Control Phase1 C>(US)         1 B     US             1     0 #>  9 RW1972 Control Phase1 C>(US)         1 C     A              1     0 #> 10 RW1972 Control Phase1 C>(US)         1 C     B              1     0 #> # ℹ 694 more rows"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"calmr_basics","text":"Let’s use plot method create plots. model supports different types plots according results can produce (e.g., associations, responses, saliences, etc.) case, RW model supports associations responses.","code":"# get all the plots for the experiment plots <- plot(my_experiment) names(plots) #> [1] \"Control - Response Strength (RW1972)\"    #> [2] \"Exp - Response Strength (RW1972)\"        #> [3] \"Control - Association Strength (RW1972)\" #> [4] \"Exp - Association Strength (RW1972)\" # or get a specific type of plot specific_plot <- plot(my_experiment, type = \"vs\") names(specific_plot) #> [1] \"Control - Association Strength (RW1972)\" #> [2] \"Exp - Association Strength (RW1972)\" # show which plots are supported by the model we are using supported_plots(\"RW1972\") #> [1] \"rs\" \"vs\""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"stimulus-associations","dir":"Articles","previous_headings":"Plotting","what":"Stimulus associations","title":"calmr_basics","text":"columns phases design rows denote source association. colors within panel determine target association.","code":"plot(my_experiment, type = \"vs\") #> $`Control - Association Strength (RW1972)` #>  #> $`Exp - Association Strength (RW1972)`"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"responding","dir":"Articles","previous_headings":"Plotting","what":"Responding","title":"calmr_basics","text":"Fairly similar , time responding function stimuli presented trial.","code":"plot(my_experiment, type = \"rs\") #> $`Control - Response Strength (RW1972)` #>  #> $`Exp - Response Strength (RW1972)`"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"graphing","dir":"Articles","previous_headings":"","what":"Graphing","title":"calmr_basics","text":"can also take look state model’s associations point training, using graph method experiment.","code":"my_graph_opts <- get_graph_opts(\"small\") graph(my_experiment, t = 20, graph_opts = my_graph_opts) #> $RW1972 #> $RW1972$`Control - Associations (RW1972)` #>  #> $RW1972$`Exp - Associations (RW1972)`"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"final-thoughts","dir":"Articles","previous_headings":"","what":"Final thoughts","title":"calmr_basics","text":"calmr package designed simulate quickly: write design get glance model predictions. said, package also features advanced R users, make sure check advanced vignettes ready.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"fitting-heidi-to-empirical-data","dir":"Articles","previous_headings":"","what":"Fitting HeiDI to empirical data","title":"calmr_fits","text":"demo, fit HeiDI empirical data (Patitucci et al., 2016, Experiment 1). involve writing function produces model responses organized empirical data, using function maximum likelihood estimation (MLE). begin short overview data, move model function, finally fit model.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"the-data","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data","what":"The data","title":"calmr_fits","text":"data (pati) contains responses (lever presses lp, nose pokes np) 32 subjects (rats) across 6 blocks training (2 sessions per block). animals trained associate two levers one two unconditioned stimuli (pellets sucrose). Let’s take look .  thicker lines group averages; rest individual subjects. ignore specific mapping levers USs , counterbalanced across subjects. However, counterbalancing end relevant (see ahead).","code":"glimpse(pati) #> Rows: 768 #> Columns: 6 #> $ subject  <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1… #> $ block    <dbl> 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3… #> $ lever    <fct> L, L, L, L, L, L, R, R, R, R, R, R, L, L, L, L, L, L, R, R, R… #> $ us       <chr> \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"S\", \"S\", \"S\", \"S\", \"S\", \"S\", \"… #> $ response <fct> lp, lp, lp, lp, lp, lp, lp, lp, lp, lp, lp, lp, np, np, np, n… #> $ rpert    <dbl> 0.9000, 1.5500, 3.3500, 4.1500, 4.6500, 4.3250, 0.4000, 0.162… pati %>% ggplot(aes(x = block, y = rpert, colour = us)) +   geom_line(aes(group = interaction(us, subject)), alpha = .3) +   stat_summary(geom = \"line\", fun = \"mean\", size = 1) +   labs(x = \"Block\", y = \"Responses per trial\", colour = \"US\") +   facet_grid(~response)"},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"writing-the-model-function","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data","what":"Writing the model function","title":"calmr_fits","text":"biggest hurdle fitting model empirical data write function , given vector parameters model arguments, generates responses organized empirical data. Let’s begin summarizing group data first, know aim . now prepare arguments model function (pass run_experimentexperiment. minor issue, HeiDI sensitive order effects. Hence, important arguments prepare reflect behavior model “general” experimental procedure, quirks unfortunate run tials. , simply address issue running several iterations model (random trial orders) average models evaluating likelihood parameters. design? experiment presented Patittuci et al. (2016) fairly simple, can reduced presentations two levers, followed different appetitive outcome. , assume two outcomes independent . also take liberties number trials specify, order reduce computing time. Note specified two counterbalancings groups. important reproduce counterbalancings data trying fit close possible. Otherwise, optimization process might latch onto experimentally-irrelevant variables. example, can seen pati lever pressing whenever lever paired pellets. didn’t counterbalance identities levers USs, optimization might result one levers less salient . can now begin write model function. First, good idea see results run_experiment returns. Although run_experiment function returns S4 class many things, care one : rs (model responses). , can write model function. Let’s dissect function three parts. put parameters (optimizer parameters) experiment using parameters method. run model select relevant information (rs). summarise model responses return .1 Let’s see function action. order empirical data model responses must match. emphasize point enough: nothing within fit function checks reorders data . sole responsible making sure pieces data order. simple way print model results return (see ). made sure everything looking good, can fit model.","code":"pati_summ <- pati %>%   group_by(block, us, response) %>%   summarise(rpert = mean(rpert), .groups = \"drop\") head(pati_summ) #> # A tibble: 6 × 4 #>   block us    response rpert #>   <dbl> <chr> <fct>    <dbl> #> 1     1 P     lp       0.820 #> 2     1 P     np       3.41  #> 3     1 S     lp       0.561 #> 4     1 S     np       3.28  #> 5     2 P     lp       1.57  #> 6     2 P     np       3.51 # The design data.frame des_df <- data.frame(   group = c(\"CB1\", \"CB2\"),   training = c(     \"12L>(Pellet)/12R>(Sucrose)\",     \"12L>(Sucrose)/12R>(Pellet)\"   ),   rand_train = TRUE ) # The parameters # the actual parameter values don't matter, # as our function will re-write them inside the optimizer call parameters <- get_parameters(des_df,   model = \"HD2022\" ) # The options options <- get_exp_opts(iterations = 4) # The arguments experiment <- make_experiment(des_df,   parameters = parameters, model = \"HD2022\",   options = options ) exp_res <- run_experiment(experiment) results(exp_res) #> $as #> # A tibble: 344 × 8 #>    model  group phase    trial_type  trial s1    block_size value #>    <chr>  <chr> <chr>    <chr>       <int> <fct>      <dbl> <dbl> #>  1 HD2022 CB1   training L>(Pellet)      1 L              2   0.4 #>  2 HD2022 CB1   training R>(Sucrose)     2 L              2   0   #>  3 HD2022 CB1   training R>(Sucrose)     3 L              2   0   #>  4 HD2022 CB1   training L>(Pellet)      4 L              2   0.4 #>  5 HD2022 CB1   training R>(Sucrose)     5 L              2   0   #>  6 HD2022 CB1   training L>(Pellet)      6 L              2   0.4 #>  7 HD2022 CB1   training L>(Pellet)      7 L              2   0.4 #>  8 HD2022 CB1   training R>(Sucrose)     8 L              2   0   #>  9 HD2022 CB1   training R>(Sucrose)     9 L              2   0   #> 10 HD2022 CB1   training L>(Pellet)     10 L              2   0.4 #> # ℹ 334 more rows #>  #> $acts #> # A tibble: 1,272 × 10 #>    model  group phase    trial_type trial s1      s2      block_size type  value #>    <chr>  <chr> <chr>    <chr>      <int> <chr>   <chr>        <dbl> <chr> <dbl> #>  1 HD2022 CB1   training L>(Pellet)     1 LPellet L                2 comb…     0 #>  2 HD2022 CB1   training L>(Pellet)     1 LPellet Pellet           2 comb…     0 #>  3 HD2022 CB1   training L>(Pellet)     1 LPellet R                2 comb…     0 #>  4 HD2022 CB1   training L>(Pellet)     1 LPellet Sucrose          2 comb…     0 #>  5 HD2022 CB1   training L>(Pellet)     1 L       L                2 chai…     0 #>  6 HD2022 CB1   training L>(Pellet)     1 L       Pellet           2 chai…     0 #>  7 HD2022 CB1   training L>(Pellet)     1 L       R                2 chai…     0 #>  8 HD2022 CB1   training L>(Pellet)     1 L       Sucrose          2 chai…     0 #>  9 HD2022 CB1   training L>(Pellet)     1 Pellet  L                2 chai…     0 #> 10 HD2022 CB1   training L>(Pellet)     1 Pellet  Pellet           2 chai…     0 #> # ℹ 1,262 more rows #>  #> $rs #> # A tibble: 1,376 × 9 #>    model  group phase    trial_type trial s1     s2      block_size value #>    <chr>  <chr> <chr>    <chr>      <int> <chr>  <chr>        <dbl> <dbl> #>  1 HD2022 CB1   training L>(Pellet)     1 L      L                2     0 #>  2 HD2022 CB1   training L>(Pellet)     1 L      Pellet           2     0 #>  3 HD2022 CB1   training L>(Pellet)     1 L      R                2     0 #>  4 HD2022 CB1   training L>(Pellet)     1 L      Sucrose          2     0 #>  5 HD2022 CB1   training L>(Pellet)     1 Pellet L                2     0 #>  6 HD2022 CB1   training L>(Pellet)     1 Pellet Pellet           2     0 #>  7 HD2022 CB1   training L>(Pellet)     1 Pellet R                2     0 #>  8 HD2022 CB1   training L>(Pellet)     1 Pellet Sucrose          2     0 #>  9 HD2022 CB1   training L>(Pellet)     1 R      L                2     0 #> 10 HD2022 CB1   training L>(Pellet)     1 R      Pellet           2     0 #> # ℹ 1,366 more rows #>  #> $vs #> # A tibble: 1,376 × 9 #>    model  group phase    trial_type trial s1     s2      block_size value #>    <chr>  <chr> <chr>    <chr>      <int> <chr>  <chr>        <dbl> <dbl> #>  1 HD2022 CB1   training L>(Pellet)     1 L      L                2     0 #>  2 HD2022 CB1   training L>(Pellet)     1 L      Pellet           2     0 #>  3 HD2022 CB1   training L>(Pellet)     1 L      R                2     0 #>  4 HD2022 CB1   training L>(Pellet)     1 L      Sucrose          2     0 #>  5 HD2022 CB1   training L>(Pellet)     1 Pellet L                2     0 #>  6 HD2022 CB1   training L>(Pellet)     1 Pellet Pellet           2     0 #>  7 HD2022 CB1   training L>(Pellet)     1 Pellet R                2     0 #>  8 HD2022 CB1   training L>(Pellet)     1 Pellet Sucrose          2     0 #>  9 HD2022 CB1   training L>(Pellet)     1 R      L                2     0 #> 10 HD2022 CB1   training L>(Pellet)     1 R      Pellet           2     0 #> # ℹ 1,366 more rows my_model_function <- function(parameters, experiment) {   # manipulating parameters   new_parameters <- parameters(experiment)[[1]]   new_parameters$alphas[] <- parameters   parameters(experiment) <- new_parameters # note parameters method   # running the model and selecting rs   exp_res <- run_experiment(experiment)   # summarizing the model   rs <- results(exp_res)$rs %>%     filter(s2 %in% c(\"Pellet\", \"Sucrose\")) %>%     mutate(       response = ifelse(s1 %in% c(\"Pellet\", \"Sucrose\"), \"np\", \"lp\"),       block = ceiling(trial / 4)     ) %>%     rowwise() %>%     # note this filter below;     # we do not allow lever presses if the lever was not presented on the trial     filter(response == \"np\" | (response == \"lp\" & grepl(s1, trial_type))) %>%     mutate(us = ifelse(grepl(\"Pellet\", trial_type), \"P\", \"S\")) %>%     group_by(block, us, response) %>%     summarise(value = mean(value), .groups = \"drop\")   rs$value } my_model_function(c(.1, .2, .4, .3), experiment = experiment) #>  [1] 0.009200000 0.003028571 0.012075000 0.005769643 0.027451634 0.010299086 #>  [7] 0.046803321 0.021698787 0.048681560 0.016592848 0.052716098 0.029575459 #> [13] 0.050783660 0.019358818 0.066233126 0.036222556 0.055000834 0.021867550 #> [19] 0.070767634 0.040360835 0.057311961 0.023442829 0.073063348 0.042882258"},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"fitting-the-model","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data","what":"Fitting the model","title":"calmr_fits","text":"fit models using fit_model function. function requires 4 arguments: (empirical) data model function arguments run model function. optimizer options. done great job taking care first three, let’s tackle last. get_optimizer_opts function returns many things: model_pars: name model parameters. , name alphas stimuli. ll ul: lower upper limits parameter search. Consider shrinking speed process. optimizer: numerical optimization technique wish use MLE estimation. family: family distribution assume model. practice, request used determine link function transform model responses, likelihood function used objective function. normal family nothing fancy model responses, estimate extra parameter, scale, scales model responses scale empirical data. comes likelihood functions, family use normal density data model differences. family_pars: family-specific parameter estimated alongside salience parameters. verbose: Whether print parameters objective function values optimize. free modify ; just make sure structure list returned get_optimizer_opts remains . can also pass extra parameters optimizer call using (e.g., par argument optim, parallel ga). , fit model parallel ga, 10 iterations. , can fit model! (patient following along) fit_model function returns lot information track put got . However, typing model console show MLE parameters obtained time negative log likelihood, given data: ’s good , well model run parameters “visually” fit data? can obtain predictions model via predict function.  looks pretty good! Save blatant misfits, course. Now know everything need fit calmr empirical data. Go forth!","code":"my_optimizer_opts <- get_optimizer_opts(   model_pars = names(parameters$alphas),   optimizer = \"ga\",   ll = c(0, 0, 0, 0),   ul = c(1, 1, 1, 1),   family = \"normal\" ) my_optimizer_opts #> $model_pars #> [1] \"L\"       \"Pellet\"  \"R\"       \"Sucrose\" #>  #> $optimizer #> [1] \"ga\" #>  #> $family #> [1] \"normal\" #>  #> $family_pars #> [1] \"normal_scale\" #>  #> $all_pars #> [1] \"L\"            \"Pellet\"       \"R\"            \"Sucrose\"      \"normal_scale\" #>  #> $initial_pars #> [1] NA NA NA NA #>  #> $ll #>            L       Pellet            R      Sucrose normal_scale  #>            0            0            0            0            0  #>  #> $ul #>            L       Pellet            R      Sucrose normal_scale  #>            1            1            1            1          100  #>  #> $verbose #> [1] FALSE the_fit <- fit_model(pati_summ$rpert,   model_function = my_model_function,   experiment = experiment,   optimizer_options = my_optimizer_opts,   maxiter = 10,   parallel = TRUE ) the_fit #> Calmr model fit:  #>   Parameters:  #>            L            R       Pellet      Sucrose normal_scale  #>    0.3393293    0.2059405    0.4570155    0.8230219   22.2735919  #>  #>  nLogLik:  #>  50.28647 pati_summ$prediction <- predict(the_fit, experiment = experiment) pati_summ %>%   rename(\"data\" = \"rpert\") %>%   pivot_longer(     cols = c(\"prediction\", \"data\"),     names_to = \"type\",     values_to = \"value\"   ) %>%   ggplot(ggplot2::aes(x = block, y = value, colour = us, linetype = type)) +   geom_line() +   theme_bw() +   facet_grid(us ~ response)"},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"a-final-note","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data > Fitting the model","what":"A final note","title":"calmr_fits","text":"vignette pre-generated, don’t want user fit model time installation. try keep package develops, spot inconsistencies, please drop line.","code":""},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"simulating-similarity-effects","dir":"Articles","previous_headings":"","what":"Simulating similarity effects","title":"heidi_similarity","text":"Honey Dwyer (2022), authors propose similarity retrieved nominal saliencies stimulus representations modulates quantities combination rule. Retrieved saliencies exclusively absent stimuli, result existing associations stimuli (see Eq. 8 math vignette). contrast, nominal saliencies denote intensity stimulus representations stimuli actually presented trial (\\(\\alpha\\)). intuitive example effect saliency similarity responding refers effect weakly retrieved representations behavior. low similarity weakly retrieved representation nominal representation result reduced effect former behavior. example, typical Pavlovian inhibition paradigm \\[(US)/AX\\], inhibitor (e.g., X) strong effect behavior actually presented weak effect behavior weakly retrieved stimulus strong association (e.g., ). Yet, inspiration proposing general rule fairly specific. attempt explain introduction delay CS US stimuli higher-order conditioning experiments sometimes enhance responding stimulus never paired US (e.g., AX/X(US) X(US)/AX).","code":""},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"reproducing-the-simulation-presented-in-the-paper","dir":"Articles","previous_headings":"Simulating similarity effects","what":"Reproducing the simulation presented in the paper","title":"heidi_similarity","text":"paper, authors plot similarity retrieved saliencies nominal saliencies stimulus X sensory preconditioning experiment short delay X US used (group Reduced) (group ). effect introducing delay simulated X saliency .36; otherwise, saliency .40. saliencies US fixed .30 .50, respectively, conditions.","code":"df <- data.frame(   Group = c(\"Same\", \"Reduced\"),   P1 = c(\"10A(X_a)\", \"10A(X_a)\"),   R1 = c(FALSE, FALSE),   P2 = c(\"10(X_a)(US)\", \"10(X_b)(US)\"),   R2 = c(FALSE, FALSE) ) params <- get_parameters(df, model = \"HD2022\") params$alphas[] <- c(.30, .40, .50, .36) model <- run_experiment(df,   model = \"HD2022\",   parameters = params, options = get_exp_opts() )"},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"plotting-the-similarity-between-saliencies","dir":"Articles","previous_headings":"Simulating similarity effects > Reproducing the simulation presented in the paper","what":"Plotting the similarity between saliencies","title":"heidi_similarity","text":"plot currently supported package, can easily generated passing \\(\\rightarrow X\\) association one alphas internal function used calculate similarity calmr:::.alphaSim.","code":"results(model)$vs %>%   filter(s1 == \"A\" & s2 == \"X\" & phase == \"P1\") %>% # using only the first phase   mutate(nominal_alpha = ifelse(group == \"Reduced\", mean(.36, .40), .40)) %>%   mutate(similarity = calmr:::.alphaSim(value, nominal_alpha)) %>%   ggplot(aes(x = trial, y = similarity, linetype = group)) +   geom_line() +   theme_bw() +   labs(x = \"Trial\", y = \"Similarity\", linetype = \"Group\")"},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"plotting-the-distribution-of-responding","dir":"Articles","previous_headings":"Simulating similarity effects > Reproducing the simulation presented in the paper","what":"Plotting the distribution of responding","title":"heidi_similarity","text":"one little bit trickier, figure manuscript effectively contains many experiments varying number XA trials starting first-order conditioning phase. address , run multiple simulations different experimental designs. Run model. now can manually plot distribution responding among stimuli model$rs.","code":"ntrials <- 1:10 df <- data.frame(   Group = c(paste0(\"S\", ntrials), paste0(\"R\", ntrials)),   P1 = rep(paste0(ntrials, \"A(X_a)\"), 2),   R1 = FALSE,   P2 = rep(c(\"10(X_a)>(US)\", \"10(X_b)>(US)\"), each = 10),   R2 = FALSE,   P3 = \"A#\",   R3 = FALSE ) head(df) #>   Group      P1    R1           P2    R2 P3    R3 #> 1    S1 1A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 2    S2 2A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 3    S3 3A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 4    S4 4A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 5    S5 5A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 6    S6 6A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE model <- run_experiment(df,   model = \"HD2022\",   parameters = params, options = get_exp_opts() ) results(model)$rs %>%   filter(phase == \"P3\" & s2 == \"US\") %>%   mutate(     trial = trial - 11,     group_lab = ifelse(substr(group, 1, 1) == \"R\", \"Reduced\", \"Same\")   ) %>%   ggplot(aes(x = trial, y = value, colour = s1, linetype = group_lab)) +   geom_line() +   theme_bw() +   labs(x = \"Trial\", y = \"R-value\", colour = \"stimulus\", linetype = \"Group\") +   facet_wrap(~s2)"},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"some-final-notes","dir":"Articles","previous_headings":"Simulating similarity effects > Reproducing the simulation presented in the paper","what":"Some final notes","title":"heidi_similarity","text":"paper, Honey Dwyer completely specify rules choosing reference value similarity calculation whenever one nominal stimulus experienced. example, simulation use two nominal versions X stimulus (X_a X_b), , whenever model compute similarity retrieved (.e., \\(\\rightarrow X\\)) conditioned saliency values, encounter problem choose among least two conditioned values (X_a X_b). Although authors paper chose saliency nominal X conditioned US (.e., X_b), specify choice made , less intuitive situations. way avoid solving issue, current implementation similarity rule uses average nominal stimuli reference value similarity calculation. specific simulation case, implementation actually reduces effect similarity distribution responding.","code":""},{"path":"https://victornavarro.org/calmr/articles/parallelism_in_calmr.html","id":"running-experiments-in-parallel","dir":"Articles","previous_headings":"","what":"Running experiments in parallel","title":"parallelism_in_calmr","text":"advent time-based models, version 0.51 calmr uses future package parallelize operations. Thanks design philosophy future, running things parallel takes single line code.","code":""},{"path":"https://victornavarro.org/calmr/articles/parallelism_in_calmr.html","id":"why-run-things-in-parallel","dir":"Articles","previous_headings":"Running experiments in parallel","what":"Why run things in parallel?","title":"parallelism_in_calmr","text":"many situations find run model many iterations, either design contains enough kinds trials order effects worry, want run model different parameters. Let’s run HeiDI model (Honey et al., 2020) long, random design. Let’s also enable verbosity via calmr_verbosity, uses cool progressr package. can see timings , elapsed column. Let’s try parallelizing now.","code":"library(calmr) # enables progress bars (try it on your computer) # calmr_verbosity(TRUE) pav_inhib <- data.frame(   group = \"group\",   phase1 = \"50(US)/50AB/50#A\",   rand1 = TRUE ) # set options to introduce more randomness pars <- get_parameters(pav_inhib, model = \"HDI2020\") opts <- get_exp_opts(iterations = 100, miniblocks = FALSE) exp <- make_experiment(pav_inhib,   parameters = pars,   model = \"HDI2020\", options = opts )  # time it start <- proc.time() pav_res <- run_experiment(exp) end <- proc.time() - start end #>    user  system elapsed  #>   5.243   0.064   3.781"},{"path":"https://victornavarro.org/calmr/articles/parallelism_in_calmr.html","id":"running-an-experiment-in-parallel","dir":"Articles","previous_headings":"Running experiments in parallel","what":"Running an experiment in parallel","title":"parallelism_in_calmr","text":"run experiment, parallel, need enable future plan. “plan” one many ways future package can parallelize things (consult documentation). Regardless, running calmr single computer, ’ll using plan(multisession) case, parallel evaluation slower. future package trades ease--use bulkier overheads. overheads tend constant, parallelization better payoff run iterations.","code":"library(future) plan(multisession)  start <- proc.time() pav_res <- run_experiment(exp) end <- proc.time() - start end #>    user  system elapsed  #>   0.625   0.095   4.039  # go back to non-parallel evaluations plan(sequential)"},{"path":"https://victornavarro.org/calmr/articles/using_time_models.html","id":"time-models-in-calmr","dir":"Articles","previous_headings":"","what":"Time models in calmr","title":"using_time_models","text":"Version 0.5 calmr introduced first time-based model, ANCCR (Jeong et al., 2022), , wrote several additional tools future models.","code":""},{"path":"https://victornavarro.org/calmr/articles/using_time_models.html","id":"changes-to-trial-based-models","dir":"Articles","previous_headings":"Time models in calmr","what":"Changes to trial-based models","title":"using_time_models","text":"biggest change calmr version 0.5 use “>” character effect trial-based models. , “>” character used specify single split within trial. example, “>(US)” encode typical situation stimulus followed US. used mimic traditional situation expect organism start (conditionally) responding US delivered. , trial-based models two steps within trial: expectation step first half trial retrieved absent stimuli, learning step, stimuli trial associated . first pass (start throwing extinction trials, better yet, probe trials test associations).","code":""},{"path":"https://victornavarro.org/calmr/articles/using_time_models.html","id":"specifying-a-design-for-time-based-models","dir":"Articles","previous_headings":"Time models in calmr","what":"Specifying a design for time-based models","title":"using_time_models","text":"designs time-based models nearly identical trial-based models. However, clever use “>” character enrich parameter list. Let’s specify serial feature discrimination experiment: now let’s get parameters ANCCR model. ANCCR model plenty parameters, yet nearly half parameters list correspond parameters use create experience model receive. leave explanation model-based parameters future. now, suffice note can control timing trials (events) using things like post_trial_delay, mean_ITI, transition_delay, etc. Let’s make model’s experience look first 20 entries. can see , several rows per trial, specifying different stimulus. Time-based models like ANCCR run time log make ample use time difference events. Let’s run model see plots.    ’s ! Easy, right?","code":"library(calmr) fpfn <- data.frame(   group = c(\"FP\", \"FN\"),   phase1 = c(\"100F>T>(US)/100T\", \"100F>T/100T>(US)\"),   r1 = c(TRUE, TRUE) ) fpfn #>   group           phase1   r1 #> 1    FP 100F>T>(US)/100T TRUE #> 2    FN 100F>T/100T>(US) TRUE pars <- get_parameters(fpfn, model = \"ANCCR\") # increase learning rates pars$alpha_reward <- 0.8 pars$alpha <- 0.08 # increase sampling interval pars$sampling_interval <- 5 pars #> $reward_magnitude #>  F  T US  #>  1  1  1  #>  #> $betas #>  F  T US  #>  1  1  1  #>  #> $cost #> [1] 0 #>  #> $temperature #> [1] 1 #>  #> $threshold #> [1] 0.6 #>  #> $k #> [1] 1 #>  #> $w #> [1] 0.5 #>  #> $minimum_rate #> [1] 0.001 #>  #> $sampling_interval #> [1] 5 #>  #> $use_exact_mean #> [1] 0 #>  #> $use_exponential #> [1] 1 #>  #> $t_ratio #> [1] 1.2 #>  #> $t_constant #> [1] NA #>  #> $t_jitter #> [1] 0.1 #>  #> $alpha #> [1] 0.08 #>  #> $alpha_reward #> [1] 0.8 #>  #> $use_timed_alpha #> [1] 0 #>  #> $alpha_exponent #> [1] 1 #>  #> $alpha_init #> [1] 1 #>  #> $alpha_min #> [1] 0 #>  #> $post_trial_delay #> F>T>(US)        T      F>T   T>(US)  #>        1        1        1        1  #>  #> $mean_ITI #> F>T>(US)        T      F>T   T>(US)  #>       30       30       30       30  #>  #> $max_ITI #> F>T>(US)        T      F>T   T>(US)  #>       90       90       90       90  #>  #> $transition_delay #> $transition_delay$`F>T>(US)` #>    F>T T>(US)  #>      1      1  #>  #> $transition_delay$`F>T` #> F>T  #>   1  #>  #> $transition_delay$`T>(US)` #> T>(US)  #>      1 experiment <- make_experiment(fpfn,   parameters = pars,   model = \"ANCCR\" ) head(experience(experiment)[[1]], 20) #>    group trial tp     tn is_test  phase block_size stimulus      time reward_mag #> 1     FN     1  4 T>(US)   FALSE phase1          2        T  19.13618          1 #> 2     FN     1  4 T>(US)   FALSE phase1          2       US  20.13618          1 #> 3     FN     2  3    F>T   FALSE phase1          2        F  21.25171          1 #> 4     FN     2  3    F>T   FALSE phase1          2        T  22.25171          1 #> 5     FN     3  4 T>(US)   FALSE phase1          2        T  98.55150          1 #> 6     FN     3  4 T>(US)   FALSE phase1          2       US  99.55150          1 #> 7     FN     4  3    F>T   FALSE phase1          2        F 162.68908          1 #> 8     FN     4  3    F>T   FALSE phase1          2        T 163.68908          1 #> 9     FN     5  3    F>T   FALSE phase1          2        F 207.74943          1 #> 10    FN     5  3    F>T   FALSE phase1          2        T 208.74943          1 #> 11    FN     6  4 T>(US)   FALSE phase1          2        T 236.62214          1 #> 12    FN     6  4 T>(US)   FALSE phase1          2       US 237.62214          1 #> 13    FN     7  4 T>(US)   FALSE phase1          2        T 259.46315          1 #> 14    FN     7  4 T>(US)   FALSE phase1          2       US 260.46315          1 #> 15    FN     8  3    F>T   FALSE phase1          2        F 272.24472          1 #> 16    FN     8  3    F>T   FALSE phase1          2        T 273.24472          1 #> 17    FN     9  3    F>T   FALSE phase1          2        F 337.99508          1 #> 18    FN     9  3    F>T   FALSE phase1          2        T 338.99508          1 #> 19    FN    10  4 T>(US)   FALSE phase1          2        T 360.41439          1 #> 20    FN    10  4 T>(US)   FALSE phase1          2       US 361.41439          1 experiment <- run_experiment(experiment) # Action values patch_plots(plot(experiment, type = \"qs\")) # ANCCR patch_plots(plot(experiment, type = \"anccrs\")) # Dopamine transients patch_plots(plot(experiment, type = \"das\"))"},{"path":[]},{"path":"https://victornavarro.org/calmr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Victor Navarro. Author, maintainer.","code":""},{"path":"https://victornavarro.org/calmr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Navarro V (2024). calmr: Canonical associative learning models R. R package version 0.5.1, https://victornavarro.org/calmr/, https://github.com/victor-navarro/calmr.","code":"@Manual{,   title = {calmr: Canonical associative learning models in R},   author = {Victor Navarro},   year = {2024},   note = {R package version 0.5.1, https://victornavarro.org/calmr/},   url = {https://github.com/victor-navarro/calmr}, }"},{"path":"https://victornavarro.org/calmr/index.html","id":"calmr","dir":"","previous_headings":"","what":"Canonical associative learning models in R","title":"Canonical associative learning models in R","text":"Canonical Associative Learning Models R","code":""},{"path":"https://victornavarro.org/calmr/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Canonical associative learning models in R","text":"need devtools install package github. , run: install.packages(\"devtools\") Afterwards, can install directly repository via: devtools::install_github(\"victor-navarro/calmr\", build_vignettes = TRUE) managed build vignettes, ’s vignette showing basics package vignette(\"calmr_basics\", package = \"calmr\") just want quick simulating, launch visual interface via:","code":"library(calmr) calmr_app()"},{"path":"https://victornavarro.org/calmr/index.html","id":"try-the-online-shiny-app","dir":"","previous_headings":"","what":"Try the online Shiny app","title":"Canonical associative learning models in R","text":"want check app without committing install, can check (wary: server might run free monthly quota). https://victor-navarro.shinyapps.io/calmr_app/","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the ANCCR model — ANCCR","title":"Train the ANCCR model — ANCCR","text":"Train ANCCR model","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the ANCCR model — ANCCR","text":"","code":"ANCCR(parameters, experience, mapping, debug = FALSE, debug_t = -1, ...)"},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the ANCCR model — ANCCR","text":"parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the ANCCR model — ANCCR","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Train the ANCCR model — ANCCR","text":"model highly experimental state. Use caution.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment CalmrDesign — augment,CalmrDesign-method","title":"Augment CalmrDesign — augment,CalmrDesign-method","text":"Methods mapping, trials, extractor functions.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment CalmrDesign — augment,CalmrDesign-method","text":"","code":"# S4 method for CalmrDesign augment(object, model, ...)  .anccrize_design(object)  # S4 method for CalmrDesign mapping(object)  # S4 method for CalmrDesign trials(object)"},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment CalmrDesign — augment,CalmrDesign-method","text":"object CalmrDesign, returned parse_design model modelname string. One supported_models() ... Additional parameters depending model. reward_labels character vector reward names","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr designs — CalmrDesign","title":"S4 class for Calmr designs — CalmrDesign","text":"Inherits tbl class","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr designs — CalmrDesign","text":"design: tbl containing object. mapping: list containing object mapping. raw_design: original data.frame. augmented: Whether object augmented.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"CalmrExperiment methods — show,CalmrExperiment-method","title":"CalmrExperiment methods — show,CalmrExperiment-method","text":"CalmrExperiment methods","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CalmrExperiment methods — show,CalmrExperiment-method","text":"","code":"# S4 method for CalmrExperiment show(object)  # S4 method for CalmrExperiment design(x)  # S4 method for CalmrExperiment arguments(x)  # S4 method for CalmrExperiment c(x, ..., recursive = FALSE)  # S4 method for CalmrExperiment parameters(x)  # S4 method for CalmrExperiment parameters(x) <- value  # S4 method for CalmrExperiment experience(x)  # S4 method for CalmrExperiment experience(x) <- value  # S4 method for CalmrExperiment results(object)  # S4 method for CalmrExperiment raw_results(object)  # S4 method for CalmrExperiment parsed_results(object)  # S4 method for CalmrExperiment length(x)  # S4 method for CalmrExperiment parse(object)  # S4 method for CalmrExperiment aggregate(x, ...)"},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment-methods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CalmrExperiment methods — show,CalmrExperiment-method","text":"","code":"# Concatenate designs df <- get_design(\"blocking\") pars1 <- get_parameters(df, model = \"RW1972\") pars2 <- get_parameters(df, model = \"MAC1975\") ex1 <- make_experiment(df, model = \"RW1972\") #> Warning: No parameters were passed. Getting default values ex2 <- make_experiment(df, model = \"MAC1975\") #> Warning: No parameters were passed. Getting default values c(ex1, ex2) #> # A tibble: 4 × 6 #>   iteration model   group    experience    mapping           parameters       #>       <int> <chr>   <chr>    <list>        <list>            <list>           #> 1         1 RW1972  Blocking <df [21 × 7]> <named list [12]> <named list [4]> #> 2         1 RW1972  Control  <df [11 × 7]> <named list [12]> <named list [4]> #> 3         1 MAC1975 Blocking <df [21 × 7]> <named list [12]> <named list [8]> #> 4         1 MAC1975 Control  <df [11 × 7]> <named list [12]> <named list [8]>  # Concatenate results lapply(c(ex1, ex2), run_experiment) #> Error in as.list.default(X): no method for coercing this S4 class to a vector"},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for calmr experiments. — CalmrExperiment-class","title":"S4 class for calmr experiments. — CalmrExperiment-class","text":"S4 class calmr experiments.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for calmr experiments. — CalmrExperiment-class","text":"arguments: tbl containing arguments run models. design: CalmrDesign object. design: CalmrExperimentResult object.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/CalmrFit-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"CalmrFit Methods — show,CalmrFit-method","title":"CalmrFit Methods — show,CalmrFit-method","text":"CalmrFit Methods","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrFit-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CalmrFit Methods — show,CalmrFit-method","text":"","code":"# S4 method for CalmrFit show(object)  # S4 method for CalmrFit predict(object, type = \"response\", ...)  # S4 method for CalmrFit NLL(object)  # S4 method for CalmrFit AIC(object, ..., k = 2)  # S4 method for CalmrFit BIC(object, ...)"},{"path":"https://victornavarro.org/calmr/reference/CalmrFit-methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CalmrFit Methods — show,CalmrFit-method","text":"AIC defined 2*k - 2*-NLL, k penalty term NLL negative log likelihood model. BIC defined k*log(n) - 2*-NLL, k number parameters model n number observations","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrFit.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr Fit — CalmrFit","title":"S4 class for Calmr Fit — CalmrFit","text":"S4 class Calmr Fit","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrFit.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr Fit — CalmrFit","text":"nloglik: Numeric. Negative log likelihood fit best_pars: Numeric. Best fitting parameters model_pars: Numeric. Parameters used model function link_pars: Numeric. Parameters used link function data: Numeric. Data used fit model_function: Function. Model function link_function: Function. Link function ll_function: Function. Objective function (usually nloglikelihood) optimizer_options: List. Options used optimizer extra_pars: List. Extra parameters passed fit call (...)","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/CalmrModel-class.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr Models — CalmrModel-class","title":"S4 class for Calmr Models — CalmrModel-class","text":"S4 class Calmr Models","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrModel-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"S4 class for Calmr Models — CalmrModel-class","text":"Currently unused.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrModel-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr Models — CalmrModel-class","text":"model model name string model_fn model function state list model internal state","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"CalmrRSA Methods — show,CalmrRSA-method","title":"CalmrRSA Methods — show,CalmrRSA-method","text":"CalmrRSA Methods","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CalmrRSA Methods — show,CalmrRSA-method","text":"","code":"# S4 method for CalmrRSA show(object)"},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr representational similarity analysis — CalmrRSA","title":"S4 class for Calmr representational similarity analysis — CalmrRSA","text":"S4 class Calmr representational similarity analysis","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr representational similarity analysis — CalmrRSA","text":"corr_mat: Array. Correlation matrix distances: List. Pairwise distance matrices arguments: List. Arguments used create object. test_data: List. Test data, populated testing object.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrResult-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Methods for CalmrResult — show,CalmrResult-method","title":"Methods for CalmrResult — show,CalmrResult-method","text":"Methods CalmrResult","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrResult-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Methods for CalmrResult — show,CalmrResult-method","text":"","code":"# S4 method for CalmrResult show(object)  # S4 method for CalmrExperimentResult c(x, ..., recursive = FALSE)"},{"path":"https://victornavarro.org/calmr/reference/CalmrResult.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr Results — CalmrResult-class","title":"S4 class for Calmr Results — CalmrResult-class","text":"S4 class Calmr Results","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrResult.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr Results — CalmrResult-class","text":"aggregated_results list fully aggregated results. parsed_results list parsed results, .e., fn(model_output). raw_results list model outputs.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the HD2022 model — HD2022","title":"Train the HD2022 model — HD2022","text":"Train HD2022 model","code":""},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the HD2022 model — HD2022","text":"","code":"HD2022(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the HD2022 model — HD2022","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the HD2022 model — HD2022","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the HDI2020 model — HDI2020","title":"Train the HDI2020 model — HDI2020","text":"Train HDI2020 model","code":""},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the HDI2020 model — HDI2020","text":"","code":"HDI2020(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the HDI2020 model — HDI2020","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the HDI2020 model — HDI2020","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the MAC1975 model — MAC1975","title":"Train the MAC1975 model — MAC1975","text":"Train MAC1975 model","code":""},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the MAC1975 model — MAC1975","text":"","code":"MAC1975(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the MAC1975 model — MAC1975","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the MAC1975 model — MAC1975","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the PKH1982 model — PKH1982","title":"Train the PKH1982 model — PKH1982","text":"Train PKH1982 model","code":""},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the PKH1982 model — PKH1982","text":"","code":"PKH1982(ev = NULL, iv = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the PKH1982 model — PKH1982","text":"ev (optional) named matrix dimensions S,S; S number stimuli. iv (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the PKH1982 model — PKH1982","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the RAND model — RAND","title":"Train the RAND model — RAND","text":"Train RAND model","code":""},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the RAND model — RAND","text":"","code":"RAND(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the RAND model — RAND","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the RAND model — RAND","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"Perform representational similarity analysis CalmrExperiment Test CalmrRSA object via permutation test","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"","code":"# S4 method for CalmrExperiment rsa(x, comparisons, test = FALSE, ...)  # S4 method for CalmrRSA test(object, n_samples = 1000, p = 0.95)  # S4 method for CalmrRSA plot(x, y, ...)"},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"x tbl m o (models outputs) aggregated results. comparisons model-named list containing model outputs compare. test Whether test RSA via permutation test. Default = FALSE. ... Additional parameters passed stats::dist stats::cor object CalmrRSA object n_samples number samples permutation test (default = 1e3) p critical threshold level permutation test (default = 0.95)","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"CalmrRSA object CalmrRSA object test results","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"object returned function can later tested via test method.","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"","code":"# Comparing the associations in three models exp <- data.frame(   Group = c(\"A\", \"B\"),   P1 = c(\"2(A)>(US)/1B>(US)\", \"1(A)>(US)/2B>(US)\"),   R1 = TRUE ) exp <- parse_design(exp) models <- c(\"HD2022\", \"RW1972\", \"PKH1982\") parameters <- sapply(models, get_parameters, design = exp) options <- get_exp_opts() exp_res <- compare_models(exp,   models = models,   parameters = parameters, options = options ) comparisons <- list(   \"HD2022\" = c(\"vs\"),   \"RW1972\" = c(\"vs\"),   \"PKH1982\" = c(\"eivs\") ) res <- rsa(exp_res, comparisons = comparisons) test(res, n_samples = 100) #> CalmrRSA object #> --------------- #> Correlation matrix: #>              HD2022.vs RW1972.vs PKH1982.eivs #> HD2022.vs    1.0000000 1.0000000    0.9326953 #> RW1972.vs    1.0000000 1.0000000    0.9326953 #> PKH1982.eivs 0.9326953 0.9326953    1.0000000 #>  #> Significance matrix: #>  #>              HD2022.vs RW1972.vs PKH1982.eivs #> HD2022.vs        FALSE     FALSE        FALSE #> RW1972.vs        FALSE     FALSE        FALSE #> PKH1982.eivs     FALSE     FALSE        FALSE #>  #> 100 permutation samples, two-tailed test with alpha = 0.05."},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the RW1972 model — RW1972","title":"Train the RW1972 model — RW1972","text":"Train RW1972 model","code":""},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the RW1972 model — RW1972","text":"","code":"RW1972(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the RW1972 model — RW1972","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the RW1972 model — RW1972","text":"CalmrModel class results <-","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the SM2007 model — SM2007","title":"Train the SM2007 model — SM2007","text":"Train SM2007 model","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the SM2007 model — SM2007","text":"","code":"SM2007(   v = NULL,   o = NULL,   parameters,   experience,   mapping,   debug = FALSE,   comparator_func = .witnauer_comparator_proc,   ... )"},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the SM2007 model — SM2007","text":"v (optional) named matrix dimensions S,S; S number stimuli. o (optional) named matrix dimensions S,S,S (operator switches). parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment debug logical specifying whether print information comparison process. Defaults FALSE. comparator_func function used comparison process. Either .witnauer_comparator_funcor .comparator_func.","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the SM2007 model — SM2007","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Train the SM2007 model — SM2007","text":"Correct usage v o parameters requires matrices adhere specific format. v, entry ,j represents association stimulus stimulus j. o, entry ,k,j represents operator switch stimulus comparator k respect stimulus j.","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the TD model — TD","title":"Train the TD model — TD","text":"Train TD model","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the TD model — TD","text":"","code":"TD(parameters, experience, mapping, debug = FALSE, debug_t = -1, ...)"},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the TD model — TD","text":"parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the TD model — TD","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Train the TD model — TD","text":"model highly experimental state. Use caution.","code":""},{"path":"https://victornavarro.org/calmr/reference/app.html","id":null,"dir":"Reference","previous_headings":"","what":"Run the calmr shiny application — app","title":"Run the calmr shiny application — app","text":"Run calmr shiny application","code":""},{"path":"https://victornavarro.org/calmr/reference/app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run the calmr shiny application — app","text":"","code":"app(browser = getOption(\"shiny.launch.browser\", interactive()))"},{"path":"https://victornavarro.org/calmr/reference/app.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run the calmr shiny application — app","text":"browser TRUE, app launched browser.","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a plot with calmr data — calmr_model_plot","title":"Create a plot with calmr data — calmr_model_plot","text":"Create plot calmr data","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a plot with calmr data — calmr_model_plot","text":"","code":"calmr_model_plot(dat, type)"},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a plot with calmr data — calmr_model_plot","text":"dat tbl data use plot type character specifying type plot","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a plot with calmr data — calmr_model_plot","text":"ggplot object","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a plot with calmr data — calmr_model_plot","text":"probably getting plots via plot method CalmrExperiments.","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":null,"dir":"Reference","previous_headings":"","what":"Set verbosity options for calmr — calmr_verbosity","title":"Set verbosity options for calmr — calmr_verbosity","text":"Whether show verbosity messages progress bars","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set verbosity options for calmr — calmr_verbosity","text":"","code":"calmr_verbosity(verbose)"},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set verbosity options for calmr — calmr_verbosity","text":"verbose logical","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set verbosity options for calmr — calmr_verbosity","text":"Progress bars handled progressr package.","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Run models given a set of parameters — compare_models","title":"Run models given a set of parameters — compare_models","text":"Run models given set parameters","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run models given a set of parameters — compare_models","text":"","code":"compare_models(design, models, parameters = NULL, options = NULL)"},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run models given a set of parameters — compare_models","text":"design design data.frame CalmrDesign models character vector length m, specifying models run. parameters list length m, entry containing list model parameters, returned get_parameters options list options, returned get_exp_opts.","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run models given a set of parameters — compare_models","text":"CalmrExperiment object","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run models given a set of parameters — compare_models","text":"","code":"exp <- data.frame(   Group = c(\"A\", \"B\"),   P1 = c(\"10(A)>(US)/5B>(US)\", \"5(A)>(US)/10B>(US)\"),   R1 = TRUE ) exp <- parse_design(exp) models <- c(\"HD2022\", \"RW1972\", \"PKH1982\") parameters <- sapply(models, get_parameters, design = exp) comp <- compare_models(exp, models = models, parameters = parameters) #> Warning: No experiment options passed. Using default options."},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit model to data — fit_model","title":"Fit model to data — fit_model","text":"Obtain MLE estimates model, given data","code":""},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit model to data — fit_model","text":"","code":"fit_model(data, model_function, optimizer_options, file = NULL, ...)"},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit model to data — fit_model","text":"data numeric vector containing data fit model . model_function function runs model returns data.frame r-values, organized data. optimizer_options list options optimizer, returned get_optimizer_opts. file path save model fit. arguments fit call found identical file, model just gets loaded. ... Extra parameters passed optimizer call","code":""},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit model to data — fit_model","text":"list nloglik: negative log-likelihood model best_pars: MLE parameters model_pars: model-specific MLE parameters link_pars: link-specific MLE parameters data: data used fit model model_function: model function supplied user link_function: link function used process ll_function: log-likelihood function used search process optimizer_options: optimizer options supplied user extra_pars: extra parameters passed optimizer call via ...","code":""},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fit model to data — fit_model","text":"See calmr_fits vignette examples","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Get basic designs — get_design","title":"Get basic designs — get_design","text":"Get basic designs","code":""},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get basic designs — get_design","text":"","code":"get_design(design_name = NULL)"},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get basic designs — get_design","text":"design_name string specifying design name (default = NULL)","code":""},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get basic designs — get_design","text":"design_name != NULL, data.frame containing design. Otherwise, list containing available designs.","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":null,"dir":"Reference","previous_headings":"","what":"Get options for an experiment — get_exp_opts","title":"Get options for an experiment — get_exp_opts","text":"Get options experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get options for an experiment — get_exp_opts","text":"","code":"get_exp_opts(iterations = 1, miniblocks = TRUE)"},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get options for an experiment — get_exp_opts","text":"iterations Numeric denoting number iterations run. Default = 1. miniblocks logical denoting whether create trial miniblocks possible. Default = TRUE.","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get options for an experiment — get_exp_opts","text":"list default user-given options.","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get options for an experiment — get_exp_opts","text":"miniblocks option direct sampling function create equally-sized miniblocks random trials within phase. example, phase string \"2A/2B\" create two miniblocks one trial. phase string \"2A/4B\" create two miniblocks one trial, 2 B trials. However, phase string \"2A/1B\" result miniblocks, even miniblocks set TRUE.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get options for an experiment — get_exp_opts","text":"","code":"get_exp_opts(iterations = 10) #> $iterations #> [1] 10 #>  #> $miniblocks #> [1] TRUE #>"},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Get model parameters — get_parameters","title":"Get model parameters — get_parameters","text":"Get model parameters","code":""},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get model parameters — get_parameters","text":"","code":"get_parameters(design, model = NULL)"},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get model parameters — get_parameters","text":"design data.frame containing experimental design. model string specifying model. One supported_models().","code":""},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get model parameters — get_parameters","text":"list model parameters depending model","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get model parameters — get_parameters","text":"","code":"block <- get_design(\"blocking\") get_parameters(block, model = \"SM2007\") #> $alphas #>   0   1   L   N  US  #> 0.4 0.4 0.4 0.4 0.4  #>  #> $lambdas #>  0  1  L  N US  #>  1  1  1  1  1  #>  #> $omegas #>   0   1   L   N  US  #> 0.2 0.2 0.2 0.2 0.2  #>  #> $rhos #>  0  1  L  N US  #>  1  1  1  1  1  #>  #> $gammas #>  0  1  L  N US  #>  1  1  1  1  1  #>  #> $taus #>   0   1   L   N  US  #> 0.2 0.2 0.2 0.2 0.2  #>  #> $order #> [1] 1 #>"},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a graph with calmr data — calmr_model_graph","title":"Create a graph with calmr data — calmr_model_graph","text":"Create graph calmr data","code":""},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a graph with calmr data — calmr_model_graph","text":"","code":"calmr_model_graph(   x,   loops = TRUE,   limits = max(abs(x$value)) * c(-1, 1),   colour_key = FALSE,   t = max(x$trial),   options = get_graph_opts(),   ... )  # S4 method for CalmrExperiment graph(x, ...)"},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a graph with calmr data — calmr_model_graph","text":"x tbl data use plot loops Logical. Whether draw arrows back forth limits Numerical. Limits color scale. Defaults NULL, case, limits set max(abs(x$value))*c(-1,1) colour_key Logical. Whether show color key options list graph options, returned get_graph_opts trial Numerical. trial graph.","code":""},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a graph with calmr data — calmr_model_graph","text":"ggplot object","code":""},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a graph with calmr data — calmr_model_graph","text":"probably getting graphs via graph method CalmrExperiments.","code":""},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Make CalmrExperiment — make_experiment","title":"Make CalmrExperiment — make_experiment","text":"Makes CalmrExperiment object containing arguments necessary run models","code":""},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make CalmrExperiment — make_experiment","text":"","code":"make_experiment(   design,   parameters = NULL,   model = NULL,   options = get_exp_opts(),   .callback_fn = NULL,   ... )"},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make CalmrExperiment — make_experiment","text":"design design data.frame parameters Parameters  model returned get_parameters model string specifying model name. One supported_models() options list options returned get_exp_opts .callback_fn function keeping track progress. Internal use. ... Extra parameters passed functions","code":""},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make CalmrExperiment — make_experiment","text":"CalmrExperiment object","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make CalmrExperiment — make_experiment","text":"","code":"des <- data.frame(Group = \"G1\", P1 = \"10A>(US)\", R1 = TRUE) ps <- get_parameters(des, model = \"HD2022\") op <- get_exp_opts(miniblocks = TRUE, iterations = 2) make_experiment(   design = des, parameters = ps,   model = \"HD2022\", options = op ) #> # A tibble: 2 × 6 #>   iteration model  group experience    mapping           parameters       #>       <int> <chr>  <chr> <list>        <list>            <list>           #> 1         1 HD2022 G1    <df [10 × 7]> <named list [12]> <named list [1]> #> 2         2 HD2022 G1    <df [10 × 7]> <named list [12]> <named list [1]>"},{"path":"https://victornavarro.org/calmr/reference/model_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Miscellaneous information functions — information_functions","title":"Miscellaneous information functions — information_functions","text":"supported_models Returns models supported package. supported_plots, model_outputs Returns plots, outputs supported argument model. supported_families Returns families supported fit_model. supported_optimizers returns optimizers supported fit_model. supported_plots returns plots supported argument model. get_model Returns function specified argument model. parameter_info Returns parameter information model (model = NULL)","code":""},{"path":"https://victornavarro.org/calmr/reference/model_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Miscellaneous information functions — information_functions","text":"","code":"supported_models()  supported_optimizers()  supported_families()  supported_plots(model = NULL)  get_model(model)  model_outputs(model = NULL)"},{"path":"https://victornavarro.org/calmr/reference/model_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Miscellaneous information functions — information_functions","text":"model string specifying model. One supported_models()","code":""},{"path":"https://victornavarro.org/calmr/reference/options.html","id":null,"dir":"Reference","previous_headings":"","what":"Get optimizer options — get_optimizer_opts","title":"Get optimizer options — get_optimizer_opts","text":"Get optimizer options","code":""},{"path":"https://victornavarro.org/calmr/reference/options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get optimizer options — get_optimizer_opts","text":"","code":"get_optimizer_opts(   model_pars,   initial_pars = rep(NA, length(model_pars)),   ll = rep(NA, length(model_pars)),   ul = rep(NA, length(model_pars)),   optimizer = NULL,   family = NULL )"},{"path":"https://victornavarro.org/calmr/reference/options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get optimizer options — get_optimizer_opts","text":"model_pars character vector specifying name parameters fit. initial_pars numeric vector specifying initial parameter values #' evaluate model (required optim). Defaults 0 parameter. ll, ul numeric vector specifying lower upper limits parameters fit, respectively optimizer string specifying optimizer use. One c(\"optim\", \"ga\") family string specifying family function generate responses (calculate likelihood function ). One c(\"identity\", \"normal\", \"poisson\").","code":""},{"path":"https://victornavarro.org/calmr/reference/options.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get optimizer options — get_optimizer_opts","text":"Whenever family function identity used, family-specific parameters always appended end relevant lists.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse design data.frame — parse_design","title":"Parse design data.frame — parse_design","text":"Parse design data.frame","code":""},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse design data.frame — parse_design","text":"","code":"parse_design(df, model = NULL, ...)"},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse design data.frame — parse_design","text":"df data.frame dimensions Groups, 2*Phases+1 model optional model augment design. See ??augment design ... arguments passed augment","code":""},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse design data.frame — parse_design","text":"CalmrDesign object","code":""},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Parse design data.frame — parse_design","text":"entry even-numbered columns df string formatted per phase_parser.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parse design data.frame — parse_design","text":"","code":"df <- data.frame(   Group = c(\"Group 1\", \"Group 2\"),   P1 = c(\"10AB(US)\", \"10A(US)\"), R1 = c(TRUE, TRUE) ) parse_design(df) #> # A tibble: 2 × 5 #>   group   phase parse_string randomize phase_info       #>   <chr>   <chr> <chr>        <lgl>     <list>           #> 1 Group 1 P1    10AB(US)     TRUE      <named list [2]> #> 2 Group 2 P1    10A(US)      TRUE      <named list [2]>"},{"path":"https://victornavarro.org/calmr/reference/patch_graphs.html","id":null,"dir":"Reference","previous_headings":"","what":"Patch Calmr graphs — patch_graphs","title":"Patch Calmr graphs — patch_graphs","text":"Convenience function patch graphs cowplot","code":""},{"path":"https://victornavarro.org/calmr/reference/patch_graphs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patch Calmr graphs — patch_graphs","text":"","code":"patch_graphs(graphs, selection = names(graphs))"},{"path":"https://victornavarro.org/calmr/reference/patch_graphs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patch Calmr graphs — patch_graphs","text":"graphs list named graphs, returned calmr::graph selection character numeric vector determining plots patch","code":""},{"path":"https://victornavarro.org/calmr/reference/patch_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Patch Calmr plots — patch_plots","title":"Patch Calmr plots — patch_plots","text":"Convenience function patch plots cowplot","code":""},{"path":"https://victornavarro.org/calmr/reference/patch_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patch Calmr plots — patch_plots","text":"","code":"patch_plots(plots, selection = NULL, plot_options = get_plot_opts())"},{"path":"https://victornavarro.org/calmr/reference/patch_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patch Calmr plots — patch_plots","text":"plots list named plots, returned calmr::plot selection character numeric vector determining plots patch plot_options list plot options returned get_plot_opts","code":""},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":null,"dir":"Reference","previous_headings":"","what":"Rat responses from Patittucci et al. 2016 — pati","title":"Rat responses from Patittucci et al. 2016 — pati","text":"dataset containing rat nose pokes lever presses levers associated different appetitive stimuli.","code":""},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rat responses from Patittucci et al. 2016 — pati","text":"","code":"pati"},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rat responses from Patittucci et al. 2016 — pati","text":"tibble following variables: subject subject identifier block 2-session block training (1 8) lever lever presented trial: L = left; R = right us stimulus followed lever: P = pellet; S = sucrose response response: lp = lever press; np = nose poke rpert responses per trial","code":""},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rat responses from Patittucci et al. 2016 — pati","text":"Patittucci et al. (2016). JEP:ALC","code":""},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":null,"dir":"Reference","previous_headings":"","what":"Parses a phase string — phase_parser","title":"Parses a phase string — phase_parser","text":"Parses phase string","code":""},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parses a phase string — phase_parser","text":"","code":"phase_parser(phase_string)"},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parses a phase string — phase_parser","text":"phase_string string","code":""},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parses a phase string — phase_parser","text":"named list","code":""},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot CalmrExperiment — plot","title":"Plot CalmrExperiment — plot","text":"Creates plots (plot) aggregated results CalmrExperiment","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot CalmrExperiment — plot","text":"","code":"plot(x, y, ...)"},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot CalmrExperiment — plot","text":"x object class CalmrExperiment. type character vector specifying types plots create. See ??supported_plots. Defaults NULL.","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot CalmrExperiment — plot","text":"ggplot object","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot CalmrExperiment — plot","text":"type = NULL, supported plots returned.","code":""},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Run experiment — run_experiment","title":"Run experiment — run_experiment","text":"Runs experiment minimal parameters.","code":""},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run experiment — run_experiment","text":"","code":"run_experiment(x, parse = TRUE, aggregate = TRUE, ...)"},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run experiment — run_experiment","text":"x CalmrExperiment design data.frame parse logical specifying whether raw results parsed. Default = TRUE. aggregate logical specifying whether parsed results aggregated. Default = TRUE. ... Arguments passed functions","code":""},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run experiment — run_experiment","text":"CalmrExperiment results.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run experiment — run_experiment","text":"","code":"# Using a data.frame only (throws warning) df <- get_design(\"relative_validity\") run_experiment(df, model = \"RW1972\") #> Warning: No parameters were passed. Getting default values #> # A tibble: 2 × 6 #>   iteration model  group  experience    mapping           parameters       #>       <int> <chr>  <chr>  <list>        <list>            <list>           #> 1         1 RW1972 Pseudo <df [21 × 7]> <named list [12]> <named list [4]> #> 2         1 RW1972 True   <df [21 × 7]> <named list [12]> <named list [4]>  # Using custom parameters df <- get_design(\"relative_validity\") pars <- get_parameters(df, model = \"HD2022\") pars$alphas[\"US\"] <- 0.6 run_experiment(df, parameters = pars, model = \"HD2022\") #> # A tibble: 2 × 6 #>   iteration model  group  experience    mapping           parameters       #>       <int> <chr>  <chr>  <list>        <list>            <list>           #> 1         1 HD2022 Pseudo <df [21 × 7]> <named list [12]> <named list [1]> #> 2         1 HD2022 True   <df [21 × 7]> <named list [12]> <named list [1]>  # Using CalmrExperiment, for more iterations df <- get_design(\"blocking\") pars <- get_parameters(df, model = \"SM2007\") exper <- make_experiment(df,   parameters = pars, model = \"SM2007\",   options = get_exp_opts(iterations = 4) ) run_experiment(exper) #> # A tibble: 8 × 6 #>   iteration model  group    experience    mapping           parameters       #>       <int> <chr>  <chr>    <list>        <list>            <list>           #> 1         1 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 2         1 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]> #> 3         2 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 4         2 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]> #> 5         3 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 6         3 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]> #> 7         4 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 8         4 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]>"},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Set reward parameters for ANCCR model — set_reward_parameters","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"Set reward parameters ANCCR model","code":""},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"","code":"set_reward_parameters(parameters, rewards = c(\"US\"))"},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"parameters list parameters, returned get_parameters rewards character vector specifying reward stimuli. Default = c(\"US\")","code":""},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"list parameters","code":""},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"default behaviour get_parameters ANCCR model set every reward-related parameter non-zero default value. function set parameters zero non-reward stimuli","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-051","dir":"Changelog","previous_headings":"","what":"calmr 0.5.1","title":"calmr 0.5.1","text":"Added parallelization progress bars via future, future.apply, progressr. Function calmr_verbosity can set verbosity package.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-050","dir":"Changelog","previous_headings":"","what":"calmr 0.5.0","title":"calmr 0.5.0","text":"Implementation ANCCR (Jeong et al., 2022), first time-based model included calmr. Added parameter distinction trial-wise period-wise parameters. Added internal augmentation design/arguments depending model. trial-based models use pre/post distinctions anymore. Using “>” special character affect models anymore. “>” special character used specify periods within trial. example, “>B>C” implies followed B followed C. See “using_time_models” vignette additional information. Named stimuli now support numbers trailing characters (e.g., “(US1)” valid now.)","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-040","dir":"Changelog","previous_headings":"","what":"calmr 0.4.0","title":"calmr 0.4.0","text":"Major refactoring classes models. help development moving forward. Added several methods access CalmrExperiment contents, including c (bind experiments) results, plot, graph, design parameters. Created CalmrDesign CalmrResult classes. Rewrote parsers less verbose rely less tidyverse suite piping. Substantially reduced complexity make_experiment function (previous make_model_args). Introduced distinction stimulus-specific global parameters. Parameters now lists instead data.frames. Modified UI calmr app include sidebar (reduce clutter). Simplified app removing options. Nearly duplicated number tests.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-030","dir":"Changelog","previous_headings":"","what":"calmr 0.3.0","title":"calmr 0.3.0","text":"Added first version SOCR model (SM2007) well two vignettes explaining math behind implementation quick simulations. Warning: EXPERIMENTAL. Documentation progress.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-020","dir":"Changelog","previous_headings":"","what":"calmr 0.2.0","title":"calmr 0.2.0","text":"Added multiple models package app (RW1972, PKH1982, MAC1975). Implementation basic S4 classes model, experiment, fit, RSA comparison objects, well methods. Added genetic algorithms (via GA) parameter estimation. Added basic tools perform representational similarity analysis. Documentation progress.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-010","dir":"Changelog","previous_headings":"","what":"calmr 0.1.0","title":"calmr 0.1.0","text":"heidi now calmr: Canonical Associative Learning Models R. package now aims maintain several associative learning models implement tools use. Major overhaul training function (train_pav_model). relevant calculations now done function functional stimuli instead just US. Support specification expectation/correction steps within trial via “>”. example, trial “>(US)” use generate expectation, learn stimuli correction step. previous plotting function R-values revamped allow simple complex versions. complex version facets r-values predictor basis, uses colour lines target. Bugfix related stimulus saliencies.","code":""}]
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This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://victornavarro.org/calmr/articles/ANCCR.html","id":"the-mathematics-behind-anccr","dir":"Articles","previous_headings":"","what":"The mathematics behind ANCCR","title":"ANCCR","text":"TODO","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"the-mathematics-behind-heidi","dir":"Articles","previous_headings":"","what":"The mathematics behind HeiDI","title":"HD2022","text":"HeiDI model four major components: 1) acquisition reciprocal associations stimuli, 2) pooling associations stimulus activations, 3) distribution activations stimulus-specific response units, 4) generation responses.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"acquiring-reciprocal-associations","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"1 - Acquiring reciprocal associations","title":"HD2022","text":"Whenever trial given, HeiDI learns associations among stimuli. association two stimuli, \\(\\) \\(j\\) denoted via \\(v_{,j}\\). association \\(v_{,j}\\) represents directional expectation: expectation \\(j\\) presented \\(\\). Furthermore, value represents nature effect \\(\\) representation \\(j\\). positive, presentation \\(\\) “excites” representation \\(j\\). negative, presentation \\(\\) “inhibits” representation \\(j\\). HeiDI learns “forward” associations stimuli, also reciprocal, “backward” associations. Thus, organisms presented \\(\\rightarrow j\\), organisms learn \\(v_{,j}\\), also \\(v_{j, }\\), expectation receiving \\(\\) presented \\(j\\). Note , sake brevity, learning equations specified forward associations.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"the-stimulus-expectation-rule","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 1 - Acquiring reciprocal associations","what":"1.1 - The stimulus expectation rule","title":"HD2022","text":"HeiDI generates expectations stimuli. expectation stimulus \\(j\\) (\\(e_j\\)) expressed \\[ \\tag{Eq. 1} e_j  = \\sum_{k}^{K}x_kv_{k,j} \\] \\(K\\) set containing stimuli experiment, \\(x_k\\) quantity denoting presence absence stimulus \\(k\\) (1 0, respectively)1.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"learning-rule","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 1 - Acquiring reciprocal associations","what":"1.2 - Learning rule","title":"HD2022","text":"HeiDI learns appropriate expectations via error-correction mechanisms. trial \\(t\\), association stimuli \\(\\) \\(j\\) expressed \\[ \\tag{Eq. 2} v_{,j, t} = v_{,j, t-1} + \\Delta v_{,j, t} \\] \\(v_{j,, t-1}\\) forward association \\(\\) \\(j\\) trial \\(t-1\\), \\(\\Delta v_{,j, t}\\) change association result trial \\(t\\). delta term uses pooled error term expressed \\[ \\tag{Eq. 3} \\Delta v_{,j} = x_i\\alpha_i(x_jc\\alpha_j - e_j) \\] \\(\\alpha_i\\) \\(\\alpha_j\\) parameters representing salience stimuli \\(\\) \\(j\\), respectively (\\(0 \\le \\alpha \\le 1\\)), \\(c\\) scaling constant (\\(c = 1\\)). Note term denoting trial, \\(t\\) omitted simplicity.","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"pooling-the-strength-of-associations","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"2 - Pooling the strength of associations","title":"HD2022","text":"HeiDI pools stimulus associations activate stimulus-specific representations. activation representation stimulus \\(j\\), \\(a_j\\), defined : \\[ \\tag{Eq. 4} a_{j,M} = o_{j,M} + h_{j,M} \\] \\(o_{j,M}\\) denotes combined associative strength towards stimulus \\(j\\) presence stimuli \\(M\\), \\(h_{j,M}\\) denotes chained associative strength towards stimulus \\(j\\) presence stimuli \\(M\\).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"combined-associative-strength","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 2 - Pooling the strength of associations","what":"2.1 - Combined associative strength","title":"HD2022","text":"quantity \\(o_{j,M}\\) result combining associative strength forwards backwards associations stimulus \\(j\\) \\[ \\tag{Eq. 5} o_{j,M} = \\sum_{m \\neq j}^{M}v_{m,j} + \\left(\\frac{\\sum_{m \\neq j}^{M}v_{m,j} \\sum_{m \\neq j}^{M}v_{j,m}}{c}\\right) \\] sums run stimuli \\(M\\) presented trial, also different stimulus \\(j\\).2 left hand term describes forward associations stimuli \\(M\\) \\(j\\) affect representation \\(j\\), whereas right hand term describes backward associations \\(j\\) stimuli \\(M\\) affect representation (although modulated forward associations ).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"chained-associative-strength","dir":"Articles","previous_headings":"The mathematics behind HeiDI > 2 - Pooling the strength of associations","what":"2.2 - Chained associative strength","title":"HD2022","text":"quantity \\(h_{j,M}\\) captures indirect associative strength stimuli \\(M\\) \\(j\\), via absent stimuli. , \\(h_{j,M}\\) defined \\[ \\tag{Eq. 6a} h_{j,M} = \\sum_{m \\neq j}^{M} \\sum_{n}^{N}\\frac{v_{m,n}o_{j,n}}{c} \\] N stimuli presented trial (.e., K-M). Note re-use \\(o\\), quantity defined Eq. 5. equation allows absent stimuli \\(N\\) influence representation stimulus \\(j\\), long association present stimuli \\(M\\). Honey Dwyer (2022), authors specify similarity-based mechanism modulates effect associative chains according similarity salience nominal retrieved stimuli. , Eq. 6a expanded : \\[ \\tag{Eq. 6b} h_{j,M} = \\sum_{m \\neq j}^{M} \\sum_{n}^{N}S(\\alpha_{n}, \\alpha'_n)\\frac{v_{m,n}o_{j,n}}{c} \\] \\(S\\) similarity function takes nominal salience stimulus n, \\(\\alpha_n\\) (perceived \\(n\\) actually presented trial) retrieved salience, \\(\\alpha'_n\\) (perceived \\(n\\) retrieved via stimuli M, see ahead). function defined : \\[ \\tag{Eq. 7} S(\\alpha_n, \\alpha'_n) = \\frac{\\alpha_n}{\\alpha_n + |\\alpha_n-\\alpha'_n|} \\times \\frac{\\alpha'_n}{\\alpha'_n+ |\\alpha_n-\\alpha'_n|} \\] Notably, whenever one nominal salience given stimulus, \\(\\alpha_n\\) arithmetic mean among nominal values (see “heidi_similarity” vignette).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"distributing-strength-into-stimulus-specific-response-units","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"3 - Distributing strength into stimulus-specific response units","title":"HD2022","text":"HeiDI distributes pooled stimulus-specific strength among \\(K\\) stimuli, according relative salience. activation response unit \\(j\\), \\(R_j\\) expressed \\[ \\tag{Eq. 8} R_{j,k} = \\frac{\\theta(j)}{\\sum_{k}^{K}\\theta(k)}a_{k,M} \\] \\(j \\K\\). \\(K\\) can include present absent stimuli, \\(\\theta\\) function depends whether stimulus \\(k\\) absent (.e., \\(k \\N\\)) (.e., \\(k \\M\\)), : \\[ \\tag{Eq. 9} \\theta(k) = \\begin{cases}     \\left |\\sum_{m}^{M}\\left( v_{m,k}+\\sum_{n \\neq k}^{N}\\frac{v_{m,n}v_{n,k}}{c}\\right) \\right|,& \\text{} k \\N\\\\     \\alpha_k, & \\text{otherwise} \\end{cases} \\] Note quantity absent stimuli absolute, order prevent negative \\(\\theta\\) values due inhibitory associations3. Also note summation term used left-hand side expression absent stimulus. implies present stimuli \\(M\\) contribute salience stimulus \\(k\\). Finally, note right side expression present stimuli contribute via direct association \\(k\\), \\(v_{m,k}\\), also associative chains absent stimuli (c.f., Eq. 6a).","code":""},{"path":"https://victornavarro.org/calmr/articles/HD2022.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind HeiDI","what":"4 - Generating responses","title":"HD2022","text":"Finally, HeiDI responds. response-generating mechanisms HeiDI currently underspecified. current version, HeiDI’s responses product activation stimulus-specific response units connection units specific motor units. , activation motor unit \\(q\\), \\(r_q\\), given \\[ \\tag{Eq. 10} r_q = R_jw_{j,q} \\] \\(w_{j,q}\\) weight representing association stimulus-specific unit \\(j\\) motor unit \\(q\\).","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"the-mathematics-behind-mac1975","dir":"Articles","previous_headings":"","what":"The mathematics behind MAC1975","title":"MAC1975","text":"grand departure global error term models RW1972 (Rescorla & Wagner, 1972), MAC1975 model (Mackintosh, 1975) uses local error terms, changes stimulus associability (\\(\\alpha\\)) via error comparison mechanism promotes learning uncertain stimuli:","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"generating-expectations","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"1 - Generating expectations","title":"MAC1975","text":"Let \\(v_{k,j}\\) denote associative strength stimulus \\(k\\) stimulus \\(j\\). given trial, expectation stimulus \\(j\\), \\(e_j\\), given : \\[ \\tag{Eq.1} e_j = \\sum_{k}^{K}x_k v_{k,j} \\] \\(x_k\\) denotes presence (1) absence (0) stimulus \\(k\\), set \\(K\\) represents stimuli design.","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"2 - Learning associations","title":"MAC1975","text":"Changes association stimulus \\(\\) \\(j\\), \\(v_{,j}\\), given : \\[ \\tag{Eq.2} \\Delta v_{,j} = x_i \\alpha_i \\beta_j (\\lambda_j - v_{,j}) \\] \\(\\alpha_i\\) associability (attention devoted ) stimulus \\(\\), \\(\\beta_j\\) learning rate parameter determined properties \\(j\\), \\(\\lambda_j\\) maximum association strength supported \\(j\\) (asymptote).","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"learning-to-attend","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"3 - Learning to attend","title":"MAC1975","text":"parameter \\(\\alpha_i\\) changes function learning, proportionally difference absolute errors conveyed \\(\\) predictors1, via: \\[ \\tag{Eq.3} \\Delta \\alpha_{} = x_i\\theta_i \\sum_{j}^{K}\\gamma_j(|\\lambda_j - \\sum_{k \\ne }^{K}v_{k,j}|-|\\lambda_j -  v_{,j}|) \\] \\(\\theta_i\\) attentional learning rate parameter stimulus \\(\\) (usually fixed across stimuli). Although Mackintosh (1975) extend model account predictive power within-compound associations, implementation model package . can sometimes result unexpected behaviour, , Eq. 3 includes extra parameter \\(\\gamma_j\\) (defaulting 1/K) denotes whether expectation stimulus \\(j\\) contributes attentional learning. , user can set parameters manually order reflect contribution different experimental stimuli. example, simple “AB>(US)” design, setting \\(\\gamma_{US}\\) = 1 \\(\\gamma_{} = \\gamma_{B} = 0\\) leads behaviour original model.","code":""},{"path":"https://victornavarro.org/calmr/articles/MAC1975.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind MAC1975","what":"4 - Generating responses","title":"MAC1975","text":"specification response-generating mechanisms MAC1975. However, simplest response function can adopted identity function stimulus expectations. , responses reflecting nature \\(j\\), \\(r_j\\), given : \\[ \\tag{Eq.4} r_j = e_j \\]","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"the-mathematics-behind-pkh1982","dir":"Articles","previous_headings":"","what":"The mathematics behind PKH1982","title":"PKH1982","text":"Another departure global error term models RW1972 (Rescorla & Wagner, 1972), PKH1982 model (Pearce et al., 1982) use error term learning excitatory associations (inhibitory associations), ties stimulus associability (\\(\\alpha\\)) absolute global prediction error. note: implementation model closely follows technical note CAL-R group possible. Divergences noted.","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"generating-expectations","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"1 - Generating expectations","title":"PKH1982","text":"Let \\(v_{k,j}\\) denote excitatory strength stimulus \\(k\\) stimulus \\(j\\), \\(v_{k,\\overline j}\\) inhibitory strength stimulus \\(k\\) stimulus \\(j\\) (effectively, “j” representation). given trial, net expectation stimulus \\(j\\), \\(e_j\\), given : \\[ \\tag{Eq.1} e_j = \\sum_{k}^{K}x_k v_{k,j} - \\sum_{k}^{K}x_k v_{k,\\overline j} \\] \\(x_k\\) denotes presence (1) absence (0) stimulus \\(k\\), set \\(K\\) represents stimuli design.","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"2 - Learning associations","title":"PKH1982","text":"Changes excitatory inhibitory associations stimuli given : \\[ \\tag{Eq.2a} \\Delta v_{,j} = \\delta_jx_i \\beta_{ex,j} \\alpha_i \\lambda_j \\] \\[ \\tag{Eq.2b} \\Delta v_{,\\overline j} = x_i \\beta_{,j} \\alpha_i  |\\overline{\\lambda_j}| \\] \\(\\beta_{ex,j}\\) \\(\\beta_{,j}\\) represent learning rates excitatory inhibitory associations, respectively, determined stimulus \\(j\\), \\(\\alpha_i\\) associability stimulus \\(\\), respectively, \\(\\lambda_j\\) \\(\\overline {\\lambda_j}\\) excitatory asymptote overexpectation stimulus \\(j\\), respectively. Importantly, \\(\\delta_j\\) Eq.2a parameter equal 1 expectation stimulus \\(j\\), lower excitatory asymptote (.e., \\(e_j < \\lambda_j\\)), 0 . implies model stops strengthening \\(v_{,j}\\) expectation \\(j\\) higher excitatory assymptote. mentioned introductory note, PKH1982 model learn excitatory associations via correction error. However, model learn inhibitory associations via correction error, overexpectation term , \\(\\overline {\\lambda_j}\\) equal \\(min(\\lambda_j - e_j, 0)\\), \\(min\\) minimum function. implies \\(\\overline {\\lambda_j}\\) takes non-zero values expectation \\(j\\) higher intensity trial (\\(\\lambda_j\\)).","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"learning-to-attend","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"3 - Learning to attend","title":"PKH1982","text":"associability parameter \\(\\alpha_i\\) changes completely trial trial function learning (note lack \\(\\Delta\\) ), change equal difference absolute global error, via: \\[ \\tag{Eq.3} \\Delta \\alpha_{} = x_i \\sum_{j}^{K}\\gamma_j(|\\lambda_j - e_j|) \\] \\(\\gamma_j\\) denotes contribution prediction error based jth stimulus. regard, important note Pearce et al. (1982) extend model account predictive power within-compound associations, yet implementation model package . can sometimes result unexpected behaviour, , Eq. 3 includes extra parameter \\(\\gamma_j\\) (defaulting 1/K) denotes whether expectation stimulus \\(j\\) contributes attentional learning. , user can set parameters manually order reflect contribution different experimental stimuli. example, simple “AB>(US)” design, setting \\(\\gamma_{US}\\) = 1 \\(\\gamma_{} = \\gamma_{B} = 0\\) leads behaviour original model. PKH1982 model improves upon Pearce & Hall (1980) model adding extra parameter controls rate associability changes. qualify changes associability determined Eq.3 via \\(\\Delta\\alpha_{}^{n}\\) (meaning happened trial \\(n\\)), can quantify total associability stimulus \\(\\) trial \\(n\\) via: \\[ \\tag{Eq.4} \\alpha_{}^{n} = \\begin{cases}   (1-\\theta_i) \\alpha_{}^{n-1} + \\theta_i\\Delta\\alpha_{j}^n &\\text{, } x_i = 1\\\\   \\alpha_{}^{n} & \\text{, otherwise} \\end{cases} \\] \\(\\theta_i\\) parameter determining rate associability decays (via \\(1-\\theta_i\\)), rate increments attention occur. Note changes associability apply stimuli presented trial (.e., \\(x_i = 1\\)); attention absent stimuli remains unchanged.","code":""},{"path":"https://victornavarro.org/calmr/articles/PKH1982.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind PKH1982","what":"4 - Generating responses","title":"PKH1982","text":"specification response-generating mechanisms PKH1982. However, simplest response function can adopted identity function stimulus expectations. , responses reflecting nature \\(j\\), \\(r_j\\), given : \\[ \\tag{Eq.5} r_j = e_j \\]","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/RAND.html","id":"the-mathematics-behind-rand","dir":"Articles","previous_headings":"","what":"The mathematics behind RAND","title":"RAND","text":"RAND model RW-based model associations randomized every trial. Therefore, model responds randomly. model meant comparisons .","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"the-mathematics-behind-rw1972","dir":"Articles","previous_headings":"","what":"The mathematics behind RW1972","title":"RW1972","text":"influential associative learning model, RW1972 (Rescorla & Wagner, 1972), learns global error posits changes stimulus associability.","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"generating-expectations","dir":"Articles","previous_headings":"The mathematics behind RW1972","what":"1 - Generating expectations","title":"RW1972","text":"Let \\(v_{k,j}\\) denote associative strength stimulus \\(k\\) stimulus \\(j\\). given trial, expectation stimulus \\(j\\), \\(e_j\\), given : \\[ \\tag{Eq.1} e_j = \\sum_{k}^{K}x_k v_{k,j} \\] \\(x_k\\) denotes presence (1) absence (0) stimulus \\(k\\), set \\(K\\) represents stimuli design.","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind RW1972","what":"2 - Learning associations","title":"RW1972","text":"Changes association stimulus \\(\\) \\(j\\), \\(v_{,j}\\), given : \\[ \\tag{Eq.2} \\Delta v_{,j} = \\alpha_i \\beta_j (\\lambda_j - e_j) \\] \\(\\alpha_i\\) associability stimulus \\(\\), \\(\\beta_j\\) learning rate parameter determined properties \\(j\\)1, \\(\\lambda_j\\) maximum association strength supported \\(j\\) (asymptote).","code":""},{"path":"https://victornavarro.org/calmr/articles/RW1972.html","id":"generating-responses","dir":"Articles","previous_headings":"The mathematics behind RW1972","what":"3 - Generating responses","title":"RW1972","text":"specification response-generating mechanisms RW1972. However, simplest response function can adopted identity function stimulus expectations. , responses reflecting nature \\(j\\), \\(r_j\\), given : \\[ \\tag{Eq.3} r_j = e_j \\]","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"the-mathematics-behind-socr","dir":"Articles","previous_headings":"","what":"The mathematics behind SOCR","title":"SM2007","text":"first formalization comparator hypothesis (Miller & Matzel, 1988), sometimes competing retrieval model (SOCR; Stout & Miller, 2007) learns local error responds function relative associative strength present retrieved stimuli.","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"learning-associations","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"1 - Learning associations","title":"SM2007","text":"SOCR model uses two different learning equations strengthening weakening associations. Whenever two stimuli contiguous, strengthening occurs. case, strengthening association stimulus \\(\\) \\(j\\) trial \\(t\\), \\(v_{,j}^t\\) given : \\[ \\tag{Eq.1a} \\Delta v_{,j}^t = x^t_i \\alpha_i \\alpha_j (\\lambda_j - v_{,j}^{t-1}) \\] \\(x^t_i\\) denotes presence (1) absence (0) stimulus \\(\\) trial \\(t\\). , SOCR model learns stimuli presented. parameters \\(\\alpha_i\\) \\(\\alpha_j\\) saliencies stimuli j, respectively, \\(\\lambda_j\\) maximum association strength supported \\(j\\) (asymptote). Whenever stimulus \\(\\) presented alone (.e., stimulus \\(j\\) absent), weakening association given : \\[ \\tag{Eq.1b} \\Delta v_{,j}^t = x_i \\alpha_i \\times -\\omega_j v_{,j}^{t-1} \\] \\(\\omega_j\\) determines weakening rate stimulus \\(j\\).1","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"activating-stimuli","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"2 - Activating stimuli","title":"SM2007","text":"SOCR posits competition stimuli presented /associatively retrieved. Dropping trial notation sake simplicity, degree stimulus \\(\\) activates stimulus \\(j\\), \\(act_{,j}\\), given : \\[ \\tag{Eq.2} act_{, j} = x_i v_{,j} + x_j\\rho_j\\alpha_j \\] \\(\\rho_j\\) (bound 0 +\\(\\infty\\)) determines much salience stimulus \\(j\\) contributes unconditioned activation. first-order activation values key quantities involved comparison processes.","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"generating-responses-and-comparison-processes","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"3 - Generating responses and comparison processes","title":"SM2007","text":"Stimulus \\(\\) generates j-oriented responding time retrieval function relative ability activate stimulus \\(j\\). relative ability expressed comparison process, given : \\[ \\tag{Eq.3} r^j_i = act_{,j} - \\Sigma_{k \\neq ,j} ^K \\gamma_k \\times o_{,k,j} \\times r^k_i \\times r^j_k \\] \\(r^j_i\\) relative activation stimulus \\(j\\) stimulus \\(\\), \\(K\\) set experimental stimuli including \\(\\) \\(j\\), \\(\\gamma_k\\) parameter determining degree stimulus \\(k\\), comparison stimulus, contributes comparison process (bound 0 1), \\(o_{,k,j}\\) operator switch determines whether \\(\\) \\(k\\) associations \\(j\\) engage facilitation competition. Finally, \\(r^k_i\\) relative activation stimulus \\(k\\) stimulus \\(\\), representing ability stimulus \\(\\) activate comparison, \\(r^j_k\\) relative activation stimulus \\(j\\) stimulus \\(k\\), representing ability comparison stimulus \\(k\\) activate stimulus \\(j\\).2 notably, last two quantities (\\(r^k_i\\) \\(r^j_k\\)) also determined corresponding instantiations Eq. 3. , involve comparison processes . number potential comparison processes technically infinite (comparison process can nest two extra comparison processes ), user must determine order model using extra global parameter (order). n-th order models (\\(n > 0\\)), model behave like extended comparator hypothesis (Denniston et al., 2001), implementing \\(n\\) comparison processes time relative activations calculated. order = 0, SM2007 behave like originally written, consider one comparison process. Indeed, n-th order models accomplished via recursion using 0-th order model stopping condition. condition reached, \\(r^k_i\\) \\(r^j_k\\) terms Eq. 3 become \\(act_{,k}\\) \\(act_{k,j}\\), respectively.","code":""},{"path":"https://victornavarro.org/calmr/articles/SM2007.html","id":"switching-between-facilitation-and-competition","dir":"Articles","previous_headings":"The mathematics behind SOCR","what":"4 - Switching between facilitation and competition","title":"SM2007","text":"operator switch Eq. 3, \\(o_{,k,j}\\), changes subjects learn discriminate directly (via \\(\\)) indirectly activated (via \\(k\\)) representations stimulus \\(j\\). change quantity depends value \\(v_{,j}\\), follows: \\[ \\tag{Eq.4} \\Delta o_{,k,j} = \\begin{cases}   \\tau_j\\alpha_iv_{,k}v_{k,j}(1-o_{,k,j})   &\\text{, } v_{,j} = 0\\\\   1-o_{,k,j} & \\text{, otherwise} \\end{cases} \\] negative values \\(o\\) indicate facilitation positive values \\(o\\) indicate competition. default value operator switches outset training set -1 default. parameter \\(\\tau_j\\) specifies learning rate operator switches related stimulus \\(j\\).","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"using-the-calmr-app","dir":"Articles","previous_headings":"","what":"Using the calmr app","title":"calmr_app","text":"beginner useR simply want simulate experimental design see model , might interested using calmr application. calmr application offers GUI allows simulate experiments without writing code. want use online app, can find : https://victor-navarro.shinyapps.io/calmr_app/. Alternatively, installed calmr package, can launch app via calmr::calmr_app(). rest tutorial assumes app open ready run. Let’s break GUI.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"design-table","dir":"Articles","previous_headings":"The GUI","what":"Design Table","title":"calmr_app","text":"design table specify experimental design run. Using Group- Group+, can remove add groups design. Using Phase- Phase+, can remove add phases design. Parse Design button used parse design, required step run simulation. later. P1 P2 columns specify phases experiment simulated left right. entry columns specifies trials given corresponding groups (G1 G2, case). entries must obey special syntax (see calmr_basics additional information). now, suffice say : number trials specified via digits left trial. example, 10A(US) specifies ten “(US)” trials. Stimuli (elements) specified letters. example, 10AB(US) specifies elements B US. Named stimuli specified within parentheses. example, (US) implies element named “US” instead compound containing elements “U” “S”. Multiple trials per phase separated via forward slash (/). Additionally, one can choose randomize trials within phase ticking boxes R1 R2 columns. important note whatever user sets interact “Create trial blocks” option Options tab sidebar (see ahead). ’s full breakdown combinations behaviour: Table checked preferences checked: Trials shuffled within blocks possible (based greater common factor). example, 2A/2B gets shuffled 2 blocks containing one one B trial, 2A/1B gets shuffled 1 block containign two trials one B trial. Table unchecked preferences checked: Trials deterministically intermixed within blocks possible. example, 2A/2B gets shuffled 2 blocks, resulting sequence ABAB. Table checked preferences unchecked: Trials shuffled completely random. Table unchecked preferences unchecked: Trials given order appearance. example, 2A/2B results AABB sequence. Go ahead parse design. new things appear GUI.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"parameters","dir":"Articles","previous_headings":"The GUI","what":"Parameters","title":"calmr_app","text":"parsing valid design. user can set parameters experiment (including stimulus-related global parameters). case, Rescorla-Wagner model 4 parameters per stimulus. default values fairly sensible, can modify parameter hand favorite spreadsheet software. parametrization model calmr can sometimes differ appears literature. following table contains links documentation pages model.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"results","dir":"Articles","previous_headings":"The GUI","what":"Results","title":"calmr_app","text":"new button appears parsing experiment. final click button run model populate “Results” “Association Graphs” portions app. Go ahead run experiment. , new button appear, allowing download results spreadsheet. calmr app, results shown visually. Clicking bar graph (one containing “Blocking - Response Strength …” ) show plots available. first portion plot’s name denotes group’s name. , plot shows strength associations among stimuli experiment across trials (blocks), faceted phases columns, origin stimuli rows. example, yellow lines denote strength B US. top column corresponds (look label right) middle column corresponds B. Go ahead explore available plots. usually self explanatory, consult documentation package case something unclear (specially using obscure models).","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"association-graphs","dir":"Articles","previous_headings":"The GUI","what":"Association Graphs","title":"calmr_app","text":"bottom portion app shows network graphs depicting strength associations model given trial, groups. Yellow denotes excitatory strength (.e., positive values), gray denotes neutral strength (.e., values close zero), purple (shown ) shows inhibitory strength (.e., negative values). Move “Trial” slider explore associations model change across experiment.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"other-bits","dir":"Articles","previous_headings":"The GUI","what":"Other bits","title":"calmr_app","text":"sections implement bulk functionalities calmr app. following sections describe additional options convenience functions found useful.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"options","dir":"Articles","previous_headings":"The GUI > Other bits","what":"Options","title":"calmr_app","text":"set number iterations run experiment (important model behaviour sensitive trial order effects), whether want create trial blocks. also set can choose plot common scale y-axis (active default).","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_app.html","id":"a-final-message","dir":"Articles","previous_headings":"The GUI","what":"A final message","title":"calmr_app","text":"Hope enjoy app! find bugs, comments, like something implemented, feel free post message package’s github repository drop line navarrov [] cardiff.ac.uk.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"your-first-simulation","dir":"Articles","previous_headings":"","what":"Your first simulation","title":"calmr_basics","text":"perform first simulation need: data.frame specifiying experiment design, list parameters model ’ll using.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"the-design-data-frame","dir":"Articles","previous_headings":"","what":"The design data.frame","title":"calmr_basics","text":"use data.frame specify experimental design. Let’s specify blocking design. rules design data.frame: row represents group. first column contains group labels. remaining columns organized pairs (trials phase, whether randomize ) trials phase column specified using rigid notation. observations : Trials preceded number. number represents number times trial given phase. “10A>(US)” means “>(US)” trial given 10 times. presence absence unconditioned stimulus denoted traditional “+” “-” symbols. Instead, use parenthesis denote “complex” stimuli. can thought element complex name (.e., one character). , “(US)” specifies single element represent US. vein, multiple characters parentheses denote individual elements. example, “AB” implies presence two stimuli, B. “>” character used separator “expectations” “correction” steps within trial. “10A>(US)” means model generates expectation , learns co-occurrence US. “/” character used trial separator (imply randomization ). Thus, “1A/1B” specifies single “” trial single “B” trial given phase. Recall randomization trials within phase specified column (, R1, R2, R3). “#” character used denote probe trials. contrast real life, probe trials entail update model’s associations. , probe trials can used track development key associations, repercussion model learns normal training trials. want check whether trial string work simulator, can use phase_parser. function returns list lot information, let’s print fields.","code":"library(calmr)  my_blocking <- data.frame(   Group = c(\"Exp\", \"Control\"),   Phase1 = c(\"10A>(US)\", \"10C>(US)\"),   R1 = c(FALSE, FALSE),   Phase2 = c(\"10AB>(US)\", \"10AB>(US)\"),   R2 = c(FALSE, FALSE),   Test = c(\"1A#/1B#\", \"1A#/1B#\"),   R3 = c(FALSE, FALSE) ) my_blocking #>     Group   Phase1    R1    Phase2    R2    Test    R3 #> 1     Exp 10A>(US) FALSE 10AB>(US) FALSE 1A#/1B# FALSE #> 2 Control 10C>(US) FALSE 10AB>(US) FALSE 1A#/1B# FALSE # not specifying a number of AB trials. Bad practice! trial <- phase_parser(\"AB/10AC\") trial$general_info[c(\"trial_names\", \"trial_repeats\")] #> $trial_names #> [1] \"AB\" \"AC\" #>  #> $trial_repeats #> [1]  1 10 # considering a configural cue for elements AB trial <- phase_parser(\"10AB(AB)(US)\") trial$general_info[c(\"func2nomi\")] #> $func2nomi #>    A    B   AB   US  #>  \"A\"  \"B\" \"AB\" \"US\""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"the-parameters-list","dir":"Articles","previous_headings":"","what":"The parameters list","title":"calmr_basics","text":"Now need pick model parameters. get models currently supported calmr, can call supported_models(). picking model, can get default parameters design get_parameters.","code":"supported_models() #> [1] \"HDI2020\" \"HD2022\"  \"RW1972\"  \"MAC1975\" \"PKH1982\" \"SM2007\"  \"RAND\"    #> [8] \"ANCCR\" my_pars <- get_parameters(my_blocking, model = \"RW1972\") # Increasing the beta parameter for US presentations my_pars$betas_on[\"US\"] <- .6 my_pars #> $alphas #>   A   B   C  US  #> 0.4 0.4 0.4 0.4  #>  #> $betas_on #>   A   B   C  US  #> 0.4 0.4 0.4 0.6  #>  #> $betas_off #>   A   B   C  US  #> 0.4 0.4 0.4 0.4  #>  #> $lambdas #>  A  B  C US  #>  1  1  1  1"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"the-optional-additional-options","dir":"Articles","previous_headings":"The parameters list","what":"The (optional) additional options","title":"calmr_basics","text":"opt , can also pass list simulation options. Simulation options entirely optional, don’t pass just get warning. can get default one get_exp_opts, modify accordingly.","code":"my_opts <- get_exp_opts() # Increase number of simulations to average out randomization effects my_opts$iterations <- 5 my_opts #> $iterations #> [1] 5 #>  #> $miniblocks #> [1] TRUE"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"simulating","dir":"Articles","previous_headings":"The parameters list","what":"Simulating","title":"calmr_basics","text":", can run simulation using run_experiment function. run_experiment returns lot information. str function best friend, plenty documentation go . course, advanced R user able dig data straight away. However, package also includes methods get quick look results.","code":"my_experiment <- run_experiment(   my_blocking,   model = \"RW1972\",   parameters = my_pars,   options = my_opts, ) slotNames(my_experiment) #> [1] \"arguments\" \"design\"    \"results\" # access arguments (see how many iterations we ran?) my_experiment@arguments #> # A tibble: 10 × 6 #>    iteration model  group   experience    mapping           parameters       #>        <int> <chr>  <chr>   <list>        <list>            <list>           #>  1         1 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  2         1 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  3         2 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  4         2 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  5         3 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  6         3 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  7         4 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #>  8         4 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> #>  9         5 RW1972 Control <df [22 × 7]> <named list [12]> <named list [4]> #> 10         5 RW1972 Exp     <df [22 × 7]> <named list [12]> <named list [4]> # access results results(my_experiment) #> $rs #> # A tibble: 704 × 9 #>    model  group   phase  trial_type trial s1    s2    block_size value #>    <chr>  <chr>   <chr>  <chr>      <int> <chr> <chr>      <dbl> <dbl> #>  1 RW1972 Control Phase1 C>(US)         1 A     A              1     0 #>  2 RW1972 Control Phase1 C>(US)         1 A     B              1     0 #>  3 RW1972 Control Phase1 C>(US)         1 A     C              1     0 #>  4 RW1972 Control Phase1 C>(US)         1 A     US             1     0 #>  5 RW1972 Control Phase1 C>(US)         1 B     A              1     0 #>  6 RW1972 Control Phase1 C>(US)         1 B     B              1     0 #>  7 RW1972 Control Phase1 C>(US)         1 B     C              1     0 #>  8 RW1972 Control Phase1 C>(US)         1 B     US             1     0 #>  9 RW1972 Control Phase1 C>(US)         1 C     A              1     0 #> 10 RW1972 Control Phase1 C>(US)         1 C     B              1     0 #> # ℹ 694 more rows #>  #> $vs #> # A tibble: 704 × 9 #>    model  group   phase  trial_type trial s1    s2    block_size value #>    <chr>  <chr>   <chr>  <chr>      <int> <chr> <chr>      <dbl> <dbl> #>  1 RW1972 Control Phase1 C>(US)         1 A     A              1     0 #>  2 RW1972 Control Phase1 C>(US)         1 A     B              1     0 #>  3 RW1972 Control Phase1 C>(US)         1 A     C              1     0 #>  4 RW1972 Control Phase1 C>(US)         1 A     US             1     0 #>  5 RW1972 Control Phase1 C>(US)         1 B     A              1     0 #>  6 RW1972 Control Phase1 C>(US)         1 B     B              1     0 #>  7 RW1972 Control Phase1 C>(US)         1 B     C              1     0 #>  8 RW1972 Control Phase1 C>(US)         1 B     US             1     0 #>  9 RW1972 Control Phase1 C>(US)         1 C     A              1     0 #> 10 RW1972 Control Phase1 C>(US)         1 C     B              1     0 #> # ℹ 694 more rows"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"calmr_basics","text":"Let’s use plot method create plots. model supports different types plots according results can produce (e.g., associations, responses, saliences, etc.) case, RW model supports associations responses.","code":"# get all the plots for the experiment plots <- plot(my_experiment) names(plots) #> [1] \"Control - Response Strength (RW1972)\"    #> [2] \"Exp - Response Strength (RW1972)\"        #> [3] \"Control - Association Strength (RW1972)\" #> [4] \"Exp - Association Strength (RW1972)\" # or get a specific type of plot specific_plot <- plot(my_experiment, type = \"vs\") names(specific_plot) #> [1] \"Control - Association Strength (RW1972)\" #> [2] \"Exp - Association Strength (RW1972)\" # show which plots are supported by the model we are using supported_plots(\"RW1972\") #> [1] \"rs\" \"vs\""},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"stimulus-associations","dir":"Articles","previous_headings":"Plotting","what":"Stimulus associations","title":"calmr_basics","text":"columns phases design rows denote source association. colors within panel determine target association.","code":"plot(my_experiment, type = \"vs\") #> $`Control - Association Strength (RW1972)` #>  #> $`Exp - Association Strength (RW1972)`"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"responding","dir":"Articles","previous_headings":"Plotting","what":"Responding","title":"calmr_basics","text":"Fairly similar , time responding function stimuli presented trial.","code":"plot(my_experiment, type = \"rs\") #> $`Control - Response Strength (RW1972)` #>  #> $`Exp - Response Strength (RW1972)`"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"graphing","dir":"Articles","previous_headings":"","what":"Graphing","title":"calmr_basics","text":"can also take look state model’s associations point training, using graph method experiment.","code":"my_graph_opts <- get_graph_opts(\"small\") graph(my_experiment, t = 20, graph_opts = my_graph_opts) #> $RW1972 #> $RW1972$`Control - Associations (RW1972)` #>  #> $RW1972$`Exp - Associations (RW1972)`"},{"path":"https://victornavarro.org/calmr/articles/calmr_basics.html","id":"final-thoughts","dir":"Articles","previous_headings":"","what":"Final thoughts","title":"calmr_basics","text":"calmr package designed simulate quickly: write design get glance model predictions. said, package also features advanced R users, make sure check advanced vignettes ready.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"fitting-heidi-to-empirical-data","dir":"Articles","previous_headings":"","what":"Fitting HeiDI to empirical data","title":"calmr_fits","text":"demo, fit HeiDI empirical data (Patitucci et al., 2016, Experiment 1). involve writing function produces model responses organized empirical data, using function maximum likelihood estimation (MLE). begin short overview data, move model function, finally fit model.","code":""},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"the-data","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data","what":"The data","title":"calmr_fits","text":"data (pati) contains responses (lever presses lp, nose pokes np) 32 subjects (rats) across 6 blocks training (2 sessions per block). animals trained associate two levers one two unconditioned stimuli (pellets sucrose). Let’s take look .  thicker lines group averages; rest individual subjects. ignore specific mapping levers USs , counterbalanced across subjects. However, counterbalancing end relevant (see ahead).","code":"glimpse(pati) #> Rows: 768 #> Columns: 6 #> $ subject  <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1… #> $ block    <dbl> 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3… #> $ lever    <fct> L, L, L, L, L, L, R, R, R, R, R, R, L, L, L, L, L, L, R, R, R… #> $ us       <chr> \"P\", \"P\", \"P\", \"P\", \"P\", \"P\", \"S\", \"S\", \"S\", \"S\", \"S\", \"S\", \"… #> $ response <fct> lp, lp, lp, lp, lp, lp, lp, lp, lp, lp, lp, lp, np, np, np, n… #> $ rpert    <dbl> 0.9000, 1.5500, 3.3500, 4.1500, 4.6500, 4.3250, 0.4000, 0.162… pati %>% ggplot(aes(x = block, y = rpert, colour = us)) +   geom_line(aes(group = interaction(us, subject)), alpha = .3) +   stat_summary(geom = \"line\", fun = \"mean\", size = 1) +   labs(x = \"Block\", y = \"Responses per trial\", colour = \"US\") +   facet_grid(~response)"},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"writing-the-model-function","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data","what":"Writing the model function","title":"calmr_fits","text":"biggest hurdle fitting model empirical data write function , given vector parameters model arguments, generates responses organized empirical data. Let’s begin summarizing group data first, know aim . now prepare arguments model function (pass run_experimentexperiment. minor issue, HeiDI sensitive order effects. Hence, important arguments prepare reflect behavior model “general” experimental procedure, quirks unfortunate run tials. , simply address issue running several iterations model (random trial orders) average models evaluating likelihood parameters. design? experiment presented Patittuci et al. (2016) fairly simple, can reduced presentations two levers, followed different appetitive outcome. , assume two outcomes independent . also take liberties number trials specify, order reduce computing time. Note specified two counterbalancings groups. important reproduce counterbalancings data trying fit close possible. Otherwise, optimization process might latch onto experimentally-irrelevant variables. example, can seen pati lever pressing whenever lever paired pellets. didn’t counterbalance identities levers USs, optimization might result one levers less salient . can now begin write model function. First, good idea see results run_experiment returns. Although run_experiment function returns S4 class many things, care one : rs (model responses). , can write model function. Let’s dissect function three parts. put parameters (optimizer parameters) experiment using parameters method. run model select relevant information (rs). summarise model responses return .1 Let’s see function action. order empirical data model responses must match. emphasize point enough: nothing within fit function checks reorders data . sole responsible making sure pieces data order. simple way print model results return (see ). made sure everything looking good, can fit model.","code":"pati_summ <- pati %>%   group_by(block, us, response) %>%   summarise(rpert = mean(rpert), .groups = \"drop\") head(pati_summ) #> # A tibble: 6 × 4 #>   block us    response rpert #>   <dbl> <chr> <fct>    <dbl> #> 1     1 P     lp       0.820 #> 2     1 P     np       3.41  #> 3     1 S     lp       0.561 #> 4     1 S     np       3.28  #> 5     2 P     lp       1.57  #> 6     2 P     np       3.51 # The design data.frame des_df <- data.frame(   group = c(\"CB1\", \"CB2\"),   training = c(     \"12L>(Pellet)/12R>(Sucrose)\",     \"12L>(Sucrose)/12R>(Pellet)\"   ),   rand_train = TRUE ) # The parameters # the actual parameter values don't matter, # as our function will re-write them inside the optimizer call parameters <- get_parameters(des_df,   model = \"HD2022\" ) # The options options <- get_exp_opts(iterations = 4) # The arguments experiment <- make_experiment(des_df,   parameters = parameters, model = \"HD2022\",   options = options ) exp_res <- run_experiment(experiment) results(exp_res) #> $as #> # A tibble: 344 × 8 #>    model  group phase    trial_type  trial s1    block_size value #>    <chr>  <chr> <chr>    <chr>       <int> <fct>      <dbl> <dbl> #>  1 HD2022 CB1   training L>(Pellet)      1 L              2   0.4 #>  2 HD2022 CB1   training R>(Sucrose)     2 L              2   0   #>  3 HD2022 CB1   training R>(Sucrose)     3 L              2   0   #>  4 HD2022 CB1   training L>(Pellet)      4 L              2   0.4 #>  5 HD2022 CB1   training R>(Sucrose)     5 L              2   0   #>  6 HD2022 CB1   training L>(Pellet)      6 L              2   0.4 #>  7 HD2022 CB1   training L>(Pellet)      7 L              2   0.4 #>  8 HD2022 CB1   training R>(Sucrose)     8 L              2   0   #>  9 HD2022 CB1   training R>(Sucrose)     9 L              2   0   #> 10 HD2022 CB1   training L>(Pellet)     10 L              2   0.4 #> # ℹ 334 more rows #>  #> $acts #> # A tibble: 1,272 × 10 #>    model  group phase    trial_type  trial s1      s2     block_size type  value #>    <chr>  <chr> <chr>    <chr>       <int> <chr>   <chr>       <dbl> <chr> <dbl> #>  1 HD2022 CB1   training L>(Pellet)      1 LPellet L               2 comb…     0 #>  2 HD2022 CB1   training L>(Pellet)      1 LPellet Pellet          2 comb…     0 #>  3 HD2022 CB1   training L>(Pellet)      1 LPellet R               2 comb…     0 #>  4 HD2022 CB1   training L>(Pellet)      1 LPellet Sucro…          2 comb…     0 #>  5 HD2022 CB1   training R>(Sucrose)     2 LPellet L               2 comb…     0 #>  6 HD2022 CB1   training R>(Sucrose)     2 LPellet Pellet          2 comb…     0 #>  7 HD2022 CB1   training R>(Sucrose)     2 LPellet R               2 comb…     0 #>  8 HD2022 CB1   training R>(Sucrose)     2 LPellet Sucro…          2 comb…     0 #>  9 HD2022 CB1   training R>(Sucrose)     3 LPellet L               2 comb…     0 #> 10 HD2022 CB1   training R>(Sucrose)     3 LPellet Pellet          2 comb…     0 #> # ℹ 1,262 more rows #>  #> $rs #> # A tibble: 1,376 × 9 #>    model  group phase    trial_type trial s1     s2      block_size value #>    <chr>  <chr> <chr>    <chr>      <int> <chr>  <chr>        <dbl> <dbl> #>  1 HD2022 CB1   training L>(Pellet)     1 L      L                2     0 #>  2 HD2022 CB1   training L>(Pellet)     1 L      Pellet           2     0 #>  3 HD2022 CB1   training L>(Pellet)     1 L      R                2     0 #>  4 HD2022 CB1   training L>(Pellet)     1 L      Sucrose          2     0 #>  5 HD2022 CB1   training L>(Pellet)     1 Pellet L                2     0 #>  6 HD2022 CB1   training L>(Pellet)     1 Pellet Pellet           2     0 #>  7 HD2022 CB1   training L>(Pellet)     1 Pellet R                2     0 #>  8 HD2022 CB1   training L>(Pellet)     1 Pellet Sucrose          2     0 #>  9 HD2022 CB1   training L>(Pellet)     1 R      L                2     0 #> 10 HD2022 CB1   training L>(Pellet)     1 R      Pellet           2     0 #> # ℹ 1,366 more rows #>  #> $vs #> # A tibble: 1,376 × 9 #>    model  group phase    trial_type trial s1     s2      block_size value #>    <chr>  <chr> <chr>    <chr>      <int> <chr>  <chr>        <dbl> <dbl> #>  1 HD2022 CB1   training L>(Pellet)     1 L      L                2     0 #>  2 HD2022 CB1   training L>(Pellet)     1 L      Pellet           2     0 #>  3 HD2022 CB1   training L>(Pellet)     1 L      R                2     0 #>  4 HD2022 CB1   training L>(Pellet)     1 L      Sucrose          2     0 #>  5 HD2022 CB1   training L>(Pellet)     1 Pellet L                2     0 #>  6 HD2022 CB1   training L>(Pellet)     1 Pellet Pellet           2     0 #>  7 HD2022 CB1   training L>(Pellet)     1 Pellet R                2     0 #>  8 HD2022 CB1   training L>(Pellet)     1 Pellet Sucrose          2     0 #>  9 HD2022 CB1   training L>(Pellet)     1 R      L                2     0 #> 10 HD2022 CB1   training L>(Pellet)     1 R      Pellet           2     0 #> # ℹ 1,366 more rows my_model_function <- function(parameters, experiment) {   # manipulating parameters   new_parameters <- parameters(experiment)[[1]]   new_parameters$alphas[] <- parameters   parameters(experiment) <- new_parameters # note parameters method   # running the model and selecting rs   exp_res <- run_experiment(experiment)   # summarizing the model   rs <- results(exp_res)$rs %>%     filter(s2 %in% c(\"Pellet\", \"Sucrose\")) %>%     mutate(       response = ifelse(s1 %in% c(\"Pellet\", \"Sucrose\"), \"np\", \"lp\"),       block = ceiling(trial / 4)     ) %>%     rowwise() %>%     # note this filter below;     # we do not allow lever presses if the lever was not presented on the trial     filter(response == \"np\" | (response == \"lp\" & grepl(s1, trial_type))) %>%     mutate(us = ifelse(grepl(\"Pellet\", trial_type), \"P\", \"S\")) %>%     group_by(block, us, response) %>%     summarise(value = mean(value), .groups = \"drop\")   rs$value } my_model_function(c(.1, .2, .4, .3), experiment = experiment) #>  [1] 0.009200000 0.003028571 0.012075000 0.005769643 0.027451634 0.010299086 #>  [7] 0.046803321 0.021698787 0.048681560 0.016592848 0.052716098 0.029575459 #> [13] 0.050783660 0.019358818 0.066233126 0.036222556 0.055000834 0.021867550 #> [19] 0.070767634 0.040360835 0.057311961 0.023442829 0.073063348 0.042882258"},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"fitting-the-model","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data","what":"Fitting the model","title":"calmr_fits","text":"fit models using fit_model function. function requires 4 arguments: (empirical) data model function arguments run model function. optimizer options. done great job taking care first three, let’s tackle last. get_optimizer_opts function returns many things: model_pars: name model parameters. , name alphas stimuli. ll ul: lower upper limits parameter search. Consider shrinking speed process. optimizer: numerical optimization technique wish use MLE estimation. family: family distribution assume model. practice, request used determine link function transform model responses, likelihood function used objective function. normal family nothing fancy model responses, estimate extra parameter, scale, scales model responses scale empirical data. comes likelihood functions, family use normal density data model differences. family_pars: family-specific parameter estimated alongside salience parameters. verbose: Whether print parameters objective function values optimize. free modify ; just make sure structure list returned get_optimizer_opts remains . can also pass extra parameters optimizer call using (e.g., par argument optim, parallel ga). , fit model parallel ga, 10 iterations. , can fit model! (patient following along) fit_model function returns lot information track put got . However, typing model console show MLE parameters obtained time negative log likelihood, given data: ’s good , well model run parameters “visually” fit data? can obtain predictions model via predict function.  looks pretty good! Save blatant misfits, course. Now know everything need fit calmr empirical data. Go forth!","code":"my_optimizer_opts <- get_optimizer_opts(   model_pars = names(parameters$alphas),   optimizer = \"ga\",   ll = c(0, 0, 0, 0),   ul = c(1, 1, 1, 1),   family = \"normal\" ) my_optimizer_opts #> $model_pars #> [1] \"L\"       \"Pellet\"  \"R\"       \"Sucrose\" #>  #> $optimizer #> [1] \"ga\" #>  #> $family #> [1] \"normal\" #>  #> $family_pars #> [1] \"normal_scale\" #>  #> $all_pars #> [1] \"L\"            \"Pellet\"       \"R\"            \"Sucrose\"      \"normal_scale\" #>  #> $initial_pars #> [1] NA NA NA NA #>  #> $ll #>            L       Pellet            R      Sucrose normal_scale  #>            0            0            0            0            0  #>  #> $ul #>            L       Pellet            R      Sucrose normal_scale  #>            1            1            1            1          100  #>  #> $verbose #> [1] FALSE the_fit <- fit_model(pati_summ$rpert,   model_function = my_model_function,   experiment = experiment,   optimizer_options = my_optimizer_opts,   maxiter = 10,   parallel = TRUE ) the_fit #> Calmr model fit:  #>   Parameters:  #>            L            R       Pellet      Sucrose normal_scale  #>    0.3393293    0.2059405    0.4570155    0.8230219   22.2735919  #>  #>  nLogLik:  #>  50.28647 pati_summ$prediction <- predict(the_fit, experiment = experiment) pati_summ %>%   rename(\"data\" = \"rpert\") %>%   pivot_longer(     cols = c(\"prediction\", \"data\"),     names_to = \"type\",     values_to = \"value\"   ) %>%   ggplot(ggplot2::aes(x = block, y = value, colour = us, linetype = type)) +   geom_line() +   theme_bw() +   facet_grid(us ~ response)"},{"path":"https://victornavarro.org/calmr/articles/calmr_fits.html","id":"a-final-note","dir":"Articles","previous_headings":"Fitting HeiDI to empirical data > Fitting the model","what":"A final note","title":"calmr_fits","text":"vignette pre-generated, don’t want user fit model time installation. try keep package develops, spot inconsistencies, please drop line.","code":""},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"simulating-similarity-effects","dir":"Articles","previous_headings":"","what":"Simulating similarity effects","title":"heidi_similarity","text":"Honey Dwyer (2022), authors propose similarity retrieved nominal saliencies stimulus representations modulates quantities combination rule. Retrieved saliencies exclusively absent stimuli, result existing associations stimuli (see Eq. 8 math vignette). contrast, nominal saliencies denote intensity stimulus representations stimuli actually presented trial (\\(\\alpha\\)). intuitive example effect saliency similarity responding refers effect weakly retrieved representations behavior. low similarity weakly retrieved representation nominal representation result reduced effect former behavior. example, typical Pavlovian inhibition paradigm \\[(US)/AX\\], inhibitor (e.g., X) strong effect behavior actually presented weak effect behavior weakly retrieved stimulus strong association (e.g., ). Yet, inspiration proposing general rule fairly specific. attempt explain introduction delay CS US stimuli higher-order conditioning experiments sometimes enhance responding stimulus never paired US (e.g., AX/X(US) X(US)/AX).","code":""},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"reproducing-the-simulation-presented-in-the-paper","dir":"Articles","previous_headings":"Simulating similarity effects","what":"Reproducing the simulation presented in the paper","title":"heidi_similarity","text":"paper, authors plot similarity retrieved saliencies nominal saliencies stimulus X sensory preconditioning experiment short delay X US used (group Reduced) (group ). effect introducing delay simulated X saliency .36; otherwise, saliency .40. saliencies US fixed .30 .50, respectively, conditions.","code":"df <- data.frame(   Group = c(\"Same\", \"Reduced\"),   P1 = c(\"10A(X_a)\", \"10A(X_a)\"),   R1 = c(FALSE, FALSE),   P2 = c(\"10(X_a)(US)\", \"10(X_b)(US)\"),   R2 = c(FALSE, FALSE) ) params <- get_parameters(df, model = \"HD2022\") params$alphas[] <- c(.30, .40, .50, .36) model <- run_experiment(df,   model = \"HD2022\",   parameters = params, options = get_exp_opts() )"},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"plotting-the-similarity-between-saliencies","dir":"Articles","previous_headings":"Simulating similarity effects > Reproducing the simulation presented in the paper","what":"Plotting the similarity between saliencies","title":"heidi_similarity","text":"plot currently supported package, can easily generated passing \\(\\rightarrow X\\) association one alphas internal function used calculate similarity calmr:::.alphaSim.","code":"results(model)$vs %>%   filter(s1 == \"A\" & s2 == \"X\" & phase == \"P1\") %>% # using only the first phase   mutate(nominal_alpha = ifelse(group == \"Reduced\", mean(.36, .40), .40)) %>%   mutate(similarity = calmr:::.alphaSim(value, nominal_alpha)) %>%   ggplot(aes(x = trial, y = similarity, linetype = group)) +   geom_line() +   theme_bw() +   labs(x = \"Trial\", y = \"Similarity\", linetype = \"Group\")"},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"plotting-the-distribution-of-responding","dir":"Articles","previous_headings":"Simulating similarity effects > Reproducing the simulation presented in the paper","what":"Plotting the distribution of responding","title":"heidi_similarity","text":"one little bit trickier, figure manuscript effectively contains many experiments varying number XA trials starting first-order conditioning phase. address , run multiple simulations different experimental designs. Run model. now can manually plot distribution responding among stimuli model$rs.","code":"ntrials <- 1:10 df <- data.frame(   Group = c(paste0(\"S\", ntrials), paste0(\"R\", ntrials)),   P1 = rep(paste0(ntrials, \"A(X_a)\"), 2),   R1 = FALSE,   P2 = rep(c(\"10(X_a)>(US)\", \"10(X_b)>(US)\"), each = 10),   R2 = FALSE,   P3 = \"A#\",   R3 = FALSE ) head(df) #>   Group      P1    R1           P2    R2 P3    R3 #> 1    S1 1A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 2    S2 2A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 3    S3 3A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 4    S4 4A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 5    S5 5A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE #> 6    S6 6A(X_a) FALSE 10(X_a)>(US) FALSE A# FALSE model <- run_experiment(df,   model = \"HD2022\",   parameters = params, options = get_exp_opts() ) results(model)$rs %>%   filter(phase == \"P3\" & s2 == \"US\") %>%   mutate(     trial = trial - 11,     group_lab = ifelse(substr(group, 1, 1) == \"R\", \"Reduced\", \"Same\")   ) %>%   ggplot(aes(x = trial, y = value, colour = s1, linetype = group_lab)) +   geom_line() +   theme_bw() +   labs(x = \"Trial\", y = \"R-value\", colour = \"stimulus\", linetype = \"Group\") +   facet_wrap(~s2)"},{"path":"https://victornavarro.org/calmr/articles/heidi_similarity.html","id":"some-final-notes","dir":"Articles","previous_headings":"Simulating similarity effects > Reproducing the simulation presented in the paper","what":"Some final notes","title":"heidi_similarity","text":"paper, Honey Dwyer completely specify rules choosing reference value similarity calculation whenever one nominal stimulus experienced. example, simulation use two nominal versions X stimulus (X_a X_b), , whenever model compute similarity retrieved (.e., \\(\\rightarrow X\\)) conditioned saliency values, encounter problem choose among least two conditioned values (X_a X_b). Although authors paper chose saliency nominal X conditioned US (.e., X_b), specify choice made , less intuitive situations. way avoid solving issue, current implementation similarity rule uses average nominal stimuli reference value similarity calculation. specific simulation case, implementation actually reduces effect similarity distribution responding.","code":""},{"path":"https://victornavarro.org/calmr/articles/parallelism_in_calmr.html","id":"running-experiments-in-parallel","dir":"Articles","previous_headings":"","what":"Running experiments in parallel","title":"parallelism_in_calmr","text":"advent time-based models, version 0.51 calmr uses future package parallelize operations. Thanks design philosophy future, running things parallel takes single line code.","code":""},{"path":"https://victornavarro.org/calmr/articles/parallelism_in_calmr.html","id":"why-run-things-in-parallel","dir":"Articles","previous_headings":"Running experiments in parallel","what":"Why run things in parallel?","title":"parallelism_in_calmr","text":"many situations find run model many iterations, either design contains enough kinds trials order effects worry, want run model different parameters. Let’s run HeiDI model (Honey et al., 2020) long, random design. Let’s also enable verbosity via calmr_verbosity, uses cool progressr package. can see timings , elapsed column. Let’s try parallelizing now.","code":"library(calmr) # enables progress bars (try it on your computer) # calmr_verbosity(TRUE) pav_inhib <- data.frame(   group = \"group\",   phase1 = \"50(US)/50AB/50#A\",   rand1 = TRUE ) # set options to introduce more randomness pars <- get_parameters(pav_inhib, model = \"HDI2020\") opts <- get_exp_opts(iterations = 100, miniblocks = FALSE) exp <- make_experiment(pav_inhib,   parameters = pars,   model = \"HDI2020\", options = opts )  # time it start <- proc.time() pav_res <- run_experiment(exp) end <- proc.time() - start end #>    user  system elapsed  #>   5.496   0.087   3.954"},{"path":"https://victornavarro.org/calmr/articles/parallelism_in_calmr.html","id":"running-an-experiment-in-parallel","dir":"Articles","previous_headings":"Running experiments in parallel","what":"Running an experiment in parallel","title":"parallelism_in_calmr","text":"run experiment, parallel, need enable future plan. “plan” one many ways future package can parallelize things (consult documentation). Regardless, running calmr single computer, ’ll using plan(multisession) case, parallel evaluation slower. future package trades ease--use bulkier overheads. overheads tend constant, parallelization better payoff run iterations.","code":"library(future) plan(multisession)  start <- proc.time() pav_res <- run_experiment(exp) end <- proc.time() - start end #>    user  system elapsed  #>   0.633   0.075   4.155  # go back to non-parallel evaluations plan(sequential)"},{"path":"https://victornavarro.org/calmr/articles/using_time_models.html","id":"time-models-in-calmr","dir":"Articles","previous_headings":"","what":"Time models in calmr","title":"using_time_models","text":"Version 0.5 calmr introduced first time-based model, ANCCR (Jeong et al., 2022), , wrote several additional tools future models.","code":""},{"path":"https://victornavarro.org/calmr/articles/using_time_models.html","id":"changes-to-trial-based-models","dir":"Articles","previous_headings":"Time models in calmr","what":"Changes to trial-based models","title":"using_time_models","text":"biggest change calmr version 0.5 use “>” character effect trial-based models. , “>” character used specify single split within trial. example, “>(US)” encode typical situation stimulus followed US. used mimic traditional situation expect organism start (conditionally) responding US delivered. , trial-based models two steps within trial: expectation step first half trial retrieved absent stimuli, learning step, stimuli trial associated . first pass (start throwing extinction trials, better yet, probe trials test associations).","code":""},{"path":"https://victornavarro.org/calmr/articles/using_time_models.html","id":"specifying-a-design-for-time-based-models","dir":"Articles","previous_headings":"Time models in calmr","what":"Specifying a design for time-based models","title":"using_time_models","text":"designs time-based models nearly identical trial-based models. However, clever use “>” character enrich parameter list. Let’s specify serial feature discrimination experiment: now let’s get parameters ANCCR model. ANCCR model plenty parameters, yet nearly half parameters list correspond parameters use create experience model receive. leave explanation model-based parameters future. now, suffice note can control timing trials (events) using things like post_trial_delay, mean_ITI, transition_delay, etc. Let’s make model’s experience look first 20 entries. can see , several rows per trial, specifying different stimulus. Time-based models like ANCCR run time log make ample use time difference events. Let’s run model see plots.    ’s ! Easy, right?","code":"library(calmr) fpfn <- data.frame(   group = c(\"FP\", \"FN\"),   phase1 = c(\"100F>T>(US)/100T\", \"100F>T/100T>(US)\"),   r1 = c(TRUE, TRUE) ) fpfn #>   group           phase1   r1 #> 1    FP 100F>T>(US)/100T TRUE #> 2    FN 100F>T/100T>(US) TRUE pars <- get_parameters(fpfn, model = \"ANCCR\") # increase learning rates pars$alpha_reward <- 0.8 pars$alpha <- 0.08 # increase sampling interval pars$sampling_interval <- 5 pars #> $reward_magnitude #>  F  T US  #>  1  1  1  #>  #> $betas #>  F  T US  #>  1  1  1  #>  #> $cost #> [1] 0 #>  #> $temperature #> [1] 1 #>  #> $threshold #> [1] 0.6 #>  #> $k #> [1] 1 #>  #> $w #> [1] 0.5 #>  #> $minimum_rate #> [1] 0.001 #>  #> $sampling_interval #> [1] 5 #>  #> $use_exact_mean #> [1] 0 #>  #> $use_exponential #> [1] 1 #>  #> $t_ratio #> [1] 1.2 #>  #> $t_constant #> [1] NA #>  #> $t_jitter #> [1] 0.1 #>  #> $alpha #> [1] 0.08 #>  #> $alpha_reward #> [1] 0.8 #>  #> $use_timed_alpha #> [1] 0 #>  #> $alpha_exponent #> [1] 1 #>  #> $alpha_init #> [1] 1 #>  #> $alpha_min #> [1] 0 #>  #> $post_trial_delay #> F>T>(US)        T      F>T   T>(US)  #>        1        1        1        1  #>  #> $mean_ITI #> F>T>(US)        T      F>T   T>(US)  #>       30       30       30       30  #>  #> $max_ITI #> F>T>(US)        T      F>T   T>(US)  #>       90       90       90       90  #>  #> $transition_delay #> $transition_delay$`F>T>(US)` #>    F>T T>(US)  #>      1      1  #>  #> $transition_delay$`F>T` #> F>T  #>   1  #>  #> $transition_delay$`T>(US)` #> T>(US)  #>      1 experiment <- make_experiment(fpfn,   parameters = pars,   model = \"ANCCR\" ) head(experience(experiment)[[1]], 20) #>    group trial tp     tn is_test  phase block_size stimulus     time reward_mag #> 1     FN     1  3    F>T   FALSE phase1          2        F  90.0000          1 #> 2     FN     1  3    F>T   FALSE phase1          2        T  91.0000          1 #> 3     FN     2  4 T>(US)   FALSE phase1          2        T 104.3101          1 #> 4     FN     2  4 T>(US)   FALSE phase1          2       US 105.3101          1 #> 5     FN     3  3    F>T   FALSE phase1          2        F 115.9003          1 #> 6     FN     3  3    F>T   FALSE phase1          2        T 116.9003          1 #> 7     FN     4  4 T>(US)   FALSE phase1          2        T 130.3224          1 #> 8     FN     4  4 T>(US)   FALSE phase1          2       US 131.3224          1 #> 9     FN     5  3    F>T   FALSE phase1          2        F 140.0717          1 #> 10    FN     5  3    F>T   FALSE phase1          2        T 141.0717          1 #> 11    FN     6  4 T>(US)   FALSE phase1          2        T 144.2789          1 #> 12    FN     6  4 T>(US)   FALSE phase1          2       US 145.2789          1 #> 13    FN     7  4 T>(US)   FALSE phase1          2        T 159.1877          1 #> 14    FN     7  4 T>(US)   FALSE phase1          2       US 160.1877          1 #> 15    FN     8  3    F>T   FALSE phase1          2        F 164.9881          1 #> 16    FN     8  3    F>T   FALSE phase1          2        T 165.9881          1 #> 17    FN     9  4 T>(US)   FALSE phase1          2        T 173.0267          1 #> 18    FN     9  4 T>(US)   FALSE phase1          2       US 174.0267          1 #> 19    FN    10  3    F>T   FALSE phase1          2        F 185.9990          1 #> 20    FN    10  3    F>T   FALSE phase1          2        T 186.9990          1 experiment <- run_experiment(experiment) # Action values patch_plots(plot(experiment, type = \"qs\")) # ANCCR patch_plots(plot(experiment, type = \"anccrs\")) # Dopamine transients patch_plots(plot(experiment, type = \"das\"))"},{"path":[]},{"path":"https://victornavarro.org/calmr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Victor Navarro. Author, maintainer.","code":""},{"path":"https://victornavarro.org/calmr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Navarro V (2024). calmr: Canonical associative learning models R. R package version 0.5.1, https://victornavarro.org/calmr/, https://github.com/victor-navarro/calmr.","code":"@Manual{,   title = {calmr: Canonical associative learning models in R},   author = {Victor Navarro},   year = {2024},   note = {R package version 0.5.1, https://victornavarro.org/calmr/},   url = {https://github.com/victor-navarro/calmr}, }"},{"path":"https://victornavarro.org/calmr/index.html","id":"calmr","dir":"","previous_headings":"","what":"Canonical associative learning models in R","title":"Canonical associative learning models in R","text":"Canonical Associative Learning Models R","code":""},{"path":"https://victornavarro.org/calmr/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Canonical associative learning models in R","text":"need devtools install package github. , run: install.packages(\"devtools\") Afterwards, can install directly repository via: devtools::install_github(\"victor-navarro/calmr\", build_vignettes = TRUE) managed build vignettes, ’s vignette showing basics package vignette(\"calmr_basics\", package = \"calmr\") just want quick simulating, launch visual interface via:","code":"library(calmr) calmr_app()"},{"path":"https://victornavarro.org/calmr/index.html","id":"try-the-online-shiny-app","dir":"","previous_headings":"","what":"Try the online Shiny app","title":"Canonical associative learning models in R","text":"want check app without committing install, can check (wary: server might run free monthly quota). https://victor-navarro.shinyapps.io/calmr_app/","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the ANCCR model — ANCCR","title":"Train the ANCCR model — ANCCR","text":"Train ANCCR model","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the ANCCR model — ANCCR","text":"","code":"ANCCR(parameters, experience, mapping, debug = FALSE, debug_t = -1, ...)"},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the ANCCR model — ANCCR","text":"parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the ANCCR model — ANCCR","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/ANCCR.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Train the ANCCR model — ANCCR","text":"model highly experimental state. Use caution.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment CalmrDesign — augment,CalmrDesign-method","title":"Augment CalmrDesign — augment,CalmrDesign-method","text":"Methods mapping, trials, extractor functions.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment CalmrDesign — augment,CalmrDesign-method","text":"","code":"# S4 method for CalmrDesign augment(object, model, ...)  .anccrize_design(object)  # S4 method for CalmrDesign mapping(object)  # S4 method for CalmrDesign trials(object)"},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment CalmrDesign — augment,CalmrDesign-method","text":"object CalmrDesign, returned parse_design model modelname string. One supported_models() ... Additional parameters depending model. reward_labels character vector reward names","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr designs — CalmrDesign","title":"S4 class for Calmr designs — CalmrDesign","text":"Inherits tbl class","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrDesign.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr designs — CalmrDesign","text":"design: tbl containing object. mapping: list containing object mapping. raw_design: original data.frame. augmented: Whether object augmented.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"CalmrExperiment methods — show,CalmrExperiment-method","title":"CalmrExperiment methods — show,CalmrExperiment-method","text":"CalmrExperiment methods","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CalmrExperiment methods — show,CalmrExperiment-method","text":"","code":"# S4 method for CalmrExperiment show(object)  # S4 method for CalmrExperiment design(x)  # S4 method for CalmrExperiment arguments(x)  # S4 method for CalmrExperiment c(x, ..., recursive = FALSE)  # S4 method for CalmrExperiment parameters(x)  # S4 method for CalmrExperiment parameters(x) <- value  # S4 method for CalmrExperiment experience(x)  # S4 method for CalmrExperiment experience(x) <- value  # S4 method for CalmrExperiment results(object)  # S4 method for CalmrExperiment raw_results(object)  # S4 method for CalmrExperiment parsed_results(object)  # S4 method for CalmrExperiment length(x)  # S4 method for CalmrExperiment parse(object)  # S4 method for CalmrExperiment aggregate(x, ...)"},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment-methods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CalmrExperiment methods — show,CalmrExperiment-method","text":"","code":"# Concatenate designs df <- get_design(\"blocking\") pars1 <- get_parameters(df, model = \"RW1972\") pars2 <- get_parameters(df, model = \"MAC1975\") ex1 <- make_experiment(df, model = \"RW1972\") #> Warning: No parameters were passed. Getting default values ex2 <- make_experiment(df, model = \"MAC1975\") #> Warning: No parameters were passed. Getting default values c(ex1, ex2) #> # A tibble: 4 × 6 #>   iteration model   group    experience    mapping           parameters       #>       <int> <chr>   <chr>    <list>        <list>            <list>           #> 1         1 RW1972  Blocking <df [21 × 7]> <named list [12]> <named list [4]> #> 2         1 RW1972  Control  <df [11 × 7]> <named list [12]> <named list [4]> #> 3         1 MAC1975 Blocking <df [21 × 7]> <named list [12]> <named list [8]> #> 4         1 MAC1975 Control  <df [11 × 7]> <named list [12]> <named list [8]>  # Concatenate results lapply(c(ex1, ex2), run_experiment) #> Error in as.list.default(X): no method for coercing this S4 class to a vector"},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for calmr experiments. — CalmrExperiment-class","title":"S4 class for calmr experiments. — CalmrExperiment-class","text":"S4 class calmr experiments.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrExperiment.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for calmr experiments. — CalmrExperiment-class","text":"arguments: tbl containing arguments run models. design: CalmrDesign object. design: CalmrExperimentResult object.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/CalmrFit-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"CalmrFit Methods — show,CalmrFit-method","title":"CalmrFit Methods — show,CalmrFit-method","text":"CalmrFit Methods","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrFit-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CalmrFit Methods — show,CalmrFit-method","text":"","code":"# S4 method for CalmrFit show(object)  # S4 method for CalmrFit predict(object, type = \"response\", ...)  # S4 method for CalmrFit NLL(object)  # S4 method for CalmrFit AIC(object, ..., k = 2)  # S4 method for CalmrFit BIC(object, ...)"},{"path":"https://victornavarro.org/calmr/reference/CalmrFit-methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CalmrFit Methods — show,CalmrFit-method","text":"AIC defined 2*k - 2*-NLL, k penalty term NLL negative log likelihood model. BIC defined k*log(n) - 2*-NLL, k number parameters model n number observations","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrFit.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr Fit — CalmrFit","title":"S4 class for Calmr Fit — CalmrFit","text":"S4 class Calmr Fit","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrFit.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr Fit — CalmrFit","text":"nloglik: Numeric. Negative log likelihood fit best_pars: Numeric. Best fitting parameters model_pars: Numeric. Parameters used model function link_pars: Numeric. Parameters used link function data: Numeric. Data used fit model_function: Function. Model function link_function: Function. Link function ll_function: Function. Objective function (usually nloglikelihood) optimizer_options: List. Options used optimizer extra_pars: List. Extra parameters passed fit call (...)","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/CalmrModel-class.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr Models — CalmrModel-class","title":"S4 class for Calmr Models — CalmrModel-class","text":"S4 class Calmr Models","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrModel-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"S4 class for Calmr Models — CalmrModel-class","text":"Currently unused.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrModel-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr Models — CalmrModel-class","text":"model model name string model_fn model function state list model internal state","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"CalmrRSA Methods — show,CalmrRSA-method","title":"CalmrRSA Methods — show,CalmrRSA-method","text":"CalmrRSA Methods","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CalmrRSA Methods — show,CalmrRSA-method","text":"","code":"# S4 method for CalmrRSA show(object)"},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr representational similarity analysis — CalmrRSA","title":"S4 class for Calmr representational similarity analysis — CalmrRSA","text":"S4 class Calmr representational similarity analysis","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrRSA.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr representational similarity analysis — CalmrRSA","text":"corr_mat: Array. Correlation matrix distances: List. Pairwise distance matrices arguments: List. Arguments used create object. test_data: List. Test data, populated testing object.","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrResult-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Methods for CalmrResult — show,CalmrResult-method","title":"Methods for CalmrResult — show,CalmrResult-method","text":"Methods CalmrResult","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrResult-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Methods for CalmrResult — show,CalmrResult-method","text":"","code":"# S4 method for CalmrResult show(object)  # S4 method for CalmrExperimentResult c(x, ..., recursive = FALSE)"},{"path":"https://victornavarro.org/calmr/reference/CalmrResult.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 class for Calmr Results — CalmrResult-class","title":"S4 class for Calmr Results — CalmrResult-class","text":"S4 class Calmr Results","code":""},{"path":"https://victornavarro.org/calmr/reference/CalmrResult.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 class for Calmr Results — CalmrResult-class","text":"aggregated_results list fully aggregated results. parsed_results list parsed results, .e., fn(model_output). raw_results list model outputs.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the HD2022 model — HD2022","title":"Train the HD2022 model — HD2022","text":"Train HD2022 model","code":""},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the HD2022 model — HD2022","text":"","code":"HD2022(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the HD2022 model — HD2022","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/HD2022.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the HD2022 model — HD2022","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the HDI2020 model — HDI2020","title":"Train the HDI2020 model — HDI2020","text":"Train HDI2020 model","code":""},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the HDI2020 model — HDI2020","text":"","code":"HDI2020(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the HDI2020 model — HDI2020","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/HDI2020.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the HDI2020 model — HDI2020","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the MAC1975 model — MAC1975","title":"Train the MAC1975 model — MAC1975","text":"Train MAC1975 model","code":""},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the MAC1975 model — MAC1975","text":"","code":"MAC1975(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the MAC1975 model — MAC1975","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/MAC1975.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the MAC1975 model — MAC1975","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the PKH1982 model — PKH1982","title":"Train the PKH1982 model — PKH1982","text":"Train PKH1982 model","code":""},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the PKH1982 model — PKH1982","text":"","code":"PKH1982(ev = NULL, iv = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the PKH1982 model — PKH1982","text":"ev (optional) named matrix dimensions S,S; S number stimuli. iv (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/PKH1982.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the PKH1982 model — PKH1982","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the RAND model — RAND","title":"Train the RAND model — RAND","text":"Train RAND model","code":""},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the RAND model — RAND","text":"","code":"RAND(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the RAND model — RAND","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/RAND.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the RAND model — RAND","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"Perform representational similarity analysis CalmrExperiment Test CalmrRSA object via permutation test","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"","code":"# S4 method for CalmrExperiment rsa(x, comparisons, test = FALSE, ...)  # S4 method for CalmrRSA test(object, n_samples = 1000, p = 0.95)  # S4 method for CalmrRSA plot(x, y, ...)"},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"x tbl m o (models outputs) aggregated results. comparisons model-named list containing model outputs compare. test Whether test RSA via permutation test. Default = FALSE. ... Additional parameters passed stats::dist stats::cor object CalmrRSA object n_samples number samples permutation test (default = 1e3) p critical threshold level permutation test (default = 0.95)","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"CalmrRSA object CalmrRSA object test results","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"object returned function can later tested via test method.","code":""},{"path":"https://victornavarro.org/calmr/reference/RSA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform representational similarity analysis on CalmrExperiment — rsa,CalmrExperiment-method","text":"","code":"# Comparing the associations in three models exp <- data.frame(   Group = c(\"A\", \"B\"),   P1 = c(\"2(A)>(US)/1B>(US)\", \"1(A)>(US)/2B>(US)\"),   R1 = TRUE ) exp <- parse_design(exp) models <- c(\"HD2022\", \"RW1972\", \"PKH1982\") parameters <- sapply(models, get_parameters, design = exp) options <- get_exp_opts() exp_res <- compare_models(exp,   models = models,   parameters = parameters, options = options ) comparisons <- list(   \"HD2022\" = c(\"vs\"),   \"RW1972\" = c(\"vs\"),   \"PKH1982\" = c(\"eivs\") ) res <- rsa(exp_res, comparisons = comparisons) test(res, n_samples = 100) #> CalmrRSA object #> --------------- #> Correlation matrix: #>               HD2022.vs  RW1972.vs PKH1982.eivs #> HD2022.vs     1.0000000  1.0000000   -0.9178532 #> RW1972.vs     1.0000000  1.0000000   -0.9178532 #> PKH1982.eivs -0.9178532 -0.9178532    1.0000000 #>  #> Significance matrix: #>  #>              HD2022.vs RW1972.vs PKH1982.eivs #> HD2022.vs        FALSE     FALSE        FALSE #> RW1972.vs        FALSE     FALSE        FALSE #> PKH1982.eivs     FALSE     FALSE        FALSE #>  #> 100 permutation samples, two-tailed test with alpha = 0.05."},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the RW1972 model — RW1972","title":"Train the RW1972 model — RW1972","text":"Train RW1972 model","code":""},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the RW1972 model — RW1972","text":"","code":"RW1972(v = NULL, parameters, experience, mapping, ...)"},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the RW1972 model — RW1972","text":"v (optional) named matrix dimensions S,S; S number stimuli. parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/RW1972.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the RW1972 model — RW1972","text":"CalmrModel class results <-","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the SM2007 model — SM2007","title":"Train the SM2007 model — SM2007","text":"Train SM2007 model","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the SM2007 model — SM2007","text":"","code":"SM2007(   v = NULL,   o = NULL,   parameters,   experience,   mapping,   debug = FALSE,   comparator_func = .witnauer_comparator_proc,   ... )"},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the SM2007 model — SM2007","text":"v (optional) named matrix dimensions S,S; S number stimuli. o (optional) named matrix dimensions S,S,S (operator switches). parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment debug logical specifying whether print information comparison process. Defaults FALSE. comparator_func function used comparison process. Either .witnauer_comparator_funcor .comparator_func.","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the SM2007 model — SM2007","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/SM2007.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Train the SM2007 model — SM2007","text":"Correct usage v o parameters requires matrices adhere specific format. v, entry ,j represents association stimulus stimulus j. o, entry ,k,j represents operator switch stimulus comparator k respect stimulus j.","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":null,"dir":"Reference","previous_headings":"","what":"Train the TD model — TD","title":"Train the TD model — TD","text":"Train TD model","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train the TD model — TD","text":"","code":"TD(parameters, experience, mapping, debug = FALSE, debug_t = -1, ...)"},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train the TD model — TD","text":"parameters list containing model parameters, returned get_parameters(). experience data.frame specifying trials rows, returned make_experiment mapping named list specifying trial stimulus mapping, returned make_experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train the TD model — TD","text":"list raw results","code":""},{"path":"https://victornavarro.org/calmr/reference/TD.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Train the TD model — TD","text":"model highly experimental state. Use caution.","code":""},{"path":"https://victornavarro.org/calmr/reference/app.html","id":null,"dir":"Reference","previous_headings":"","what":"Run the calmr shiny application — app","title":"Run the calmr shiny application — app","text":"Run calmr shiny application","code":""},{"path":"https://victornavarro.org/calmr/reference/app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run the calmr shiny application — app","text":"","code":"app(browser = getOption(\"shiny.launch.browser\", interactive()))"},{"path":"https://victornavarro.org/calmr/reference/app.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run the calmr shiny application — app","text":"browser TRUE, app launched browser.","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a plot with calmr data — calmr_model_plot","title":"Create a plot with calmr data — calmr_model_plot","text":"Create plot calmr data","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a plot with calmr data — calmr_model_plot","text":"","code":"calmr_model_plot(dat, type)"},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a plot with calmr data — calmr_model_plot","text":"dat tbl data use plot type character specifying type plot","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a plot with calmr data — calmr_model_plot","text":"ggplot object","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_model_plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a plot with calmr data — calmr_model_plot","text":"probably getting plots via plot method CalmrExperiments.","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":null,"dir":"Reference","previous_headings":"","what":"Set verbosity options for calmr — calmr_verbosity","title":"Set verbosity options for calmr — calmr_verbosity","text":"Whether show verbosity messages progress bars","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set verbosity options for calmr — calmr_verbosity","text":"","code":"calmr_verbosity(verbose)"},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set verbosity options for calmr — calmr_verbosity","text":"verbose logical","code":""},{"path":"https://victornavarro.org/calmr/reference/calmr_verbosity.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set verbosity options for calmr — calmr_verbosity","text":"Progress bars handled progressr package.","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":null,"dir":"Reference","previous_headings":"","what":"Run models given a set of parameters — compare_models","title":"Run models given a set of parameters — compare_models","text":"Run models given set parameters","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run models given a set of parameters — compare_models","text":"","code":"compare_models(design, models, parameters = NULL, options = NULL)"},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run models given a set of parameters — compare_models","text":"design design data.frame CalmrDesign models character vector length m, specifying models run. parameters list length m, entry containing list model parameters, returned get_parameters options list options, returned get_exp_opts.","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run models given a set of parameters — compare_models","text":"CalmrExperiment object","code":""},{"path":"https://victornavarro.org/calmr/reference/compare_models.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run models given a set of parameters — compare_models","text":"","code":"exp <- data.frame(   Group = c(\"A\", \"B\"),   P1 = c(\"10(A)>(US)/5B>(US)\", \"5(A)>(US)/10B>(US)\"),   R1 = TRUE ) exp <- parse_design(exp) models <- c(\"HD2022\", \"RW1972\", \"PKH1982\") parameters <- sapply(models, get_parameters, design = exp) comp <- compare_models(exp, models = models, parameters = parameters) #> Warning: No experiment options passed. Using default options."},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit model to data — fit_model","title":"Fit model to data — fit_model","text":"Obtain MLE estimates model, given data","code":""},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit model to data — fit_model","text":"","code":"fit_model(data, model_function, optimizer_options, file = NULL, ...)"},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit model to data — fit_model","text":"data numeric vector containing data fit model . model_function function runs model returns data.frame r-values, organized data. optimizer_options list options optimizer, returned get_optimizer_opts. file path save model fit. arguments fit call found identical file, model just gets loaded. ... Extra parameters passed optimizer call","code":""},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit model to data — fit_model","text":"list nloglik: negative log-likelihood model best_pars: MLE parameters model_pars: model-specific MLE parameters link_pars: link-specific MLE parameters data: data used fit model model_function: model function supplied user link_function: link function used process ll_function: log-likelihood function used search process optimizer_options: optimizer options supplied user extra_pars: extra parameters passed optimizer call via ...","code":""},{"path":"https://victornavarro.org/calmr/reference/fit_model.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fit model to data — fit_model","text":"See calmr_fits vignette examples","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Get basic designs — get_design","title":"Get basic designs — get_design","text":"Get basic designs","code":""},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get basic designs — get_design","text":"","code":"get_design(design_name = NULL)"},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get basic designs — get_design","text":"design_name string specifying design name (default = NULL)","code":""},{"path":"https://victornavarro.org/calmr/reference/get_design.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get basic designs — get_design","text":"design_name != NULL, data.frame containing design. Otherwise, list containing available designs.","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":null,"dir":"Reference","previous_headings":"","what":"Get options for an experiment — get_exp_opts","title":"Get options for an experiment — get_exp_opts","text":"Get options experiment","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get options for an experiment — get_exp_opts","text":"","code":"get_exp_opts(iterations = 1, miniblocks = TRUE)"},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get options for an experiment — get_exp_opts","text":"iterations Numeric denoting number iterations run. Default = 1. miniblocks logical denoting whether create trial miniblocks possible. Default = TRUE.","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get options for an experiment — get_exp_opts","text":"list default user-given options.","code":""},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get options for an experiment — get_exp_opts","text":"miniblocks option direct sampling function create equally-sized miniblocks random trials within phase. example, phase string \"2A/2B\" create two miniblocks one trial. phase string \"2A/4B\" create two miniblocks one trial, 2 B trials. However, phase string \"2A/1B\" result miniblocks, even miniblocks set TRUE.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/get_exp_opts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get options for an experiment — get_exp_opts","text":"","code":"get_exp_opts(iterations = 10) #> $iterations #> [1] 10 #>  #> $miniblocks #> [1] TRUE #>"},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Get model parameters — get_parameters","title":"Get model parameters — get_parameters","text":"Get model parameters","code":""},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get model parameters — get_parameters","text":"","code":"get_parameters(design, model = NULL)"},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get model parameters — get_parameters","text":"design data.frame containing experimental design. model string specifying model. One supported_models().","code":""},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get model parameters — get_parameters","text":"list model parameters depending model","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/get_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get model parameters — get_parameters","text":"","code":"block <- get_design(\"blocking\") get_parameters(block, model = \"SM2007\") #> $alphas #>   0   1   L   N  US  #> 0.4 0.4 0.4 0.4 0.4  #>  #> $lambdas #>  0  1  L  N US  #>  1  1  1  1  1  #>  #> $omegas #>   0   1   L   N  US  #> 0.2 0.2 0.2 0.2 0.2  #>  #> $rhos #>  0  1  L  N US  #>  1  1  1  1  1  #>  #> $gammas #>  0  1  L  N US  #>  1  1  1  1  1  #>  #> $taus #>   0   1   L   N  US  #> 0.2 0.2 0.2 0.2 0.2  #>  #> $order #> [1] 1 #>"},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a graph with calmr data — calmr_model_graph","title":"Create a graph with calmr data — calmr_model_graph","text":"Create graph calmr data","code":""},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a graph with calmr data — calmr_model_graph","text":"","code":"calmr_model_graph(   x,   loops = TRUE,   limits = max(abs(x$value)) * c(-1, 1),   colour_key = FALSE,   t = max(x$trial),   options = get_graph_opts(),   ... )  # S4 method for CalmrExperiment graph(x, ...)"},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a graph with calmr data — calmr_model_graph","text":"x tbl data use plot loops Logical. Whether draw arrows back forth limits Numerical. Limits color scale. Defaults NULL, case, limits set max(abs(x$value))*c(-1,1) colour_key Logical. Whether show color key options list graph options, returned get_graph_opts trial Numerical. trial graph.","code":""},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a graph with calmr data — calmr_model_graph","text":"ggplot object","code":""},{"path":"https://victornavarro.org/calmr/reference/graph.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a graph with calmr data — calmr_model_graph","text":"probably getting graphs via graph method CalmrExperiments.","code":""},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Make CalmrExperiment — make_experiment","title":"Make CalmrExperiment — make_experiment","text":"Makes CalmrExperiment object containing arguments necessary run models","code":""},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make CalmrExperiment — make_experiment","text":"","code":"make_experiment(   design,   parameters = NULL,   model = NULL,   options = get_exp_opts(),   .callback_fn = NULL,   ... )"},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make CalmrExperiment — make_experiment","text":"design design data.frame parameters Parameters  model returned get_parameters model string specifying model name. One supported_models() options list options returned get_exp_opts .callback_fn function keeping track progress. Internal use. ... Extra parameters passed functions","code":""},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make CalmrExperiment — make_experiment","text":"CalmrExperiment object","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/make_experiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make CalmrExperiment — make_experiment","text":"","code":"des <- data.frame(Group = \"G1\", P1 = \"10A>(US)\", R1 = TRUE) ps <- get_parameters(des, model = \"HD2022\") op <- get_exp_opts(miniblocks = TRUE, iterations = 2) make_experiment(   design = des, parameters = ps,   model = \"HD2022\", options = op ) #> # A tibble: 2 × 6 #>   iteration model  group experience    mapping           parameters       #>       <int> <chr>  <chr> <list>        <list>            <list>           #> 1         1 HD2022 G1    <df [10 × 7]> <named list [12]> <named list [1]> #> 2         2 HD2022 G1    <df [10 × 7]> <named list [12]> <named list [1]>"},{"path":"https://victornavarro.org/calmr/reference/model_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Miscellaneous information functions — information_functions","title":"Miscellaneous information functions — information_functions","text":"supported_models Returns models supported package. supported_plots, model_outputs Returns plots, outputs supported argument model. supported_families Returns families supported fit_model. supported_optimizers returns optimizers supported fit_model. supported_plots returns plots supported argument model. get_model Returns function specified argument model. parameter_info Returns parameter information model (model = NULL)","code":""},{"path":"https://victornavarro.org/calmr/reference/model_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Miscellaneous information functions — information_functions","text":"","code":"supported_models()  supported_optimizers()  supported_families()  supported_plots(model = NULL)  get_model(model)  model_outputs(model = NULL)"},{"path":"https://victornavarro.org/calmr/reference/model_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Miscellaneous information functions — information_functions","text":"model string specifying model. One supported_models()","code":""},{"path":"https://victornavarro.org/calmr/reference/options.html","id":null,"dir":"Reference","previous_headings":"","what":"Get optimizer options — get_optimizer_opts","title":"Get optimizer options — get_optimizer_opts","text":"Get optimizer options","code":""},{"path":"https://victornavarro.org/calmr/reference/options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get optimizer options — get_optimizer_opts","text":"","code":"get_optimizer_opts(   model_pars,   initial_pars = rep(NA, length(model_pars)),   ll = rep(NA, length(model_pars)),   ul = rep(NA, length(model_pars)),   optimizer = NULL,   family = NULL )"},{"path":"https://victornavarro.org/calmr/reference/options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get optimizer options — get_optimizer_opts","text":"model_pars character vector specifying name parameters fit. initial_pars numeric vector specifying initial parameter values #' evaluate model (required optim). Defaults 0 parameter. ll, ul numeric vector specifying lower upper limits parameters fit, respectively optimizer string specifying optimizer use. One c(\"optim\", \"ga\") family string specifying family function generate responses (calculate likelihood function ). One c(\"identity\", \"normal\", \"poisson\").","code":""},{"path":"https://victornavarro.org/calmr/reference/options.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get optimizer options — get_optimizer_opts","text":"Whenever family function identity used, family-specific parameters always appended end relevant lists.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse design data.frame — parse_design","title":"Parse design data.frame — parse_design","text":"Parse design data.frame","code":""},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse design data.frame — parse_design","text":"","code":"parse_design(df, model = NULL, ...)"},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse design data.frame — parse_design","text":"df data.frame dimensions Groups, 2*Phases+1 model optional model augment design. See ??augment design ... arguments passed augment","code":""},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse design data.frame — parse_design","text":"CalmrDesign object","code":""},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Parse design data.frame — parse_design","text":"entry even-numbered columns df string formatted per phase_parser.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/parse_design.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Parse design data.frame — parse_design","text":"","code":"df <- data.frame(   Group = c(\"Group 1\", \"Group 2\"),   P1 = c(\"10AB(US)\", \"10A(US)\"), R1 = c(TRUE, TRUE) ) parse_design(df) #> # A tibble: 2 × 5 #>   group   phase parse_string randomize phase_info       #>   <chr>   <chr> <chr>        <lgl>     <list>           #> 1 Group 1 P1    10AB(US)     TRUE      <named list [2]> #> 2 Group 2 P1    10A(US)      TRUE      <named list [2]>"},{"path":"https://victornavarro.org/calmr/reference/patch_graphs.html","id":null,"dir":"Reference","previous_headings":"","what":"Patch Calmr graphs — patch_graphs","title":"Patch Calmr graphs — patch_graphs","text":"Convenience function patch graphs cowplot","code":""},{"path":"https://victornavarro.org/calmr/reference/patch_graphs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patch Calmr graphs — patch_graphs","text":"","code":"patch_graphs(graphs, selection = names(graphs))"},{"path":"https://victornavarro.org/calmr/reference/patch_graphs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patch Calmr graphs — patch_graphs","text":"graphs list named graphs, returned calmr::graph selection character numeric vector determining plots patch","code":""},{"path":"https://victornavarro.org/calmr/reference/patch_plots.html","id":null,"dir":"Reference","previous_headings":"","what":"Patch Calmr plots — patch_plots","title":"Patch Calmr plots — patch_plots","text":"Convenience function patch plots cowplot","code":""},{"path":"https://victornavarro.org/calmr/reference/patch_plots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patch Calmr plots — patch_plots","text":"","code":"patch_plots(plots, selection = NULL, plot_options = get_plot_opts())"},{"path":"https://victornavarro.org/calmr/reference/patch_plots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Patch Calmr plots — patch_plots","text":"plots list named plots, returned calmr::plot selection character numeric vector determining plots patch plot_options list plot options returned get_plot_opts","code":""},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":null,"dir":"Reference","previous_headings":"","what":"Rat responses from Patittucci et al. 2016 — pati","title":"Rat responses from Patittucci et al. 2016 — pati","text":"dataset containing rat nose pokes lever presses levers associated different appetitive stimuli.","code":""},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rat responses from Patittucci et al. 2016 — pati","text":"","code":"pati"},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Rat responses from Patittucci et al. 2016 — pati","text":"tibble following variables: subject subject identifier block 2-session block training (1 8) lever lever presented trial: L = left; R = right us stimulus followed lever: P = pellet; S = sucrose response response: lp = lever press; np = nose poke rpert responses per trial","code":""},{"path":"https://victornavarro.org/calmr/reference/pati.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Rat responses from Patittucci et al. 2016 — pati","text":"Patittucci et al. (2016). JEP:ALC","code":""},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":null,"dir":"Reference","previous_headings":"","what":"Parses a phase string — phase_parser","title":"Parses a phase string — phase_parser","text":"Parses phase string","code":""},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parses a phase string — phase_parser","text":"","code":"phase_parser(phase_string)"},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parses a phase string — phase_parser","text":"phase_string string","code":""},{"path":"https://victornavarro.org/calmr/reference/phase_parser.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parses a phase string — phase_parser","text":"named list","code":""},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://victornavarro.org/calmr/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot CalmrExperiment — plot","title":"Plot CalmrExperiment — plot","text":"Creates plots (plot) aggregated results CalmrExperiment","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot CalmrExperiment — plot","text":"","code":"plot(x, y, ...)"},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot CalmrExperiment — plot","text":"x object class CalmrExperiment. type character vector specifying types plots create. See ??supported_plots. Defaults NULL.","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot CalmrExperiment — plot","text":"ggplot object","code":""},{"path":"https://victornavarro.org/calmr/reference/plot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Plot CalmrExperiment — plot","text":"type = NULL, supported plots returned.","code":""},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Run experiment — run_experiment","title":"Run experiment — run_experiment","text":"Runs experiment minimal parameters.","code":""},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run experiment — run_experiment","text":"","code":"run_experiment(x, parse = TRUE, aggregate = TRUE, ...)"},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run experiment — run_experiment","text":"x CalmrExperiment design data.frame parse logical specifying whether raw results parsed. Default = TRUE. aggregate logical specifying whether parsed results aggregated. Default = TRUE. ... Arguments passed functions","code":""},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run experiment — run_experiment","text":"CalmrExperiment results.","code":""},{"path":[]},{"path":"https://victornavarro.org/calmr/reference/run_experiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run experiment — run_experiment","text":"","code":"# Using a data.frame only (throws warning) df <- get_design(\"relative_validity\") run_experiment(df, model = \"RW1972\") #> Warning: No parameters were passed. Getting default values #> # A tibble: 2 × 6 #>   iteration model  group  experience    mapping           parameters       #>       <int> <chr>  <chr>  <list>        <list>            <list>           #> 1         1 RW1972 Pseudo <df [21 × 7]> <named list [12]> <named list [4]> #> 2         1 RW1972 True   <df [21 × 7]> <named list [12]> <named list [4]>  # Using custom parameters df <- get_design(\"relative_validity\") pars <- get_parameters(df, model = \"HD2022\") pars$alphas[\"US\"] <- 0.6 run_experiment(df, parameters = pars, model = \"HD2022\") #> # A tibble: 2 × 6 #>   iteration model  group  experience    mapping           parameters       #>       <int> <chr>  <chr>  <list>        <list>            <list>           #> 1         1 HD2022 Pseudo <df [21 × 7]> <named list [12]> <named list [1]> #> 2         1 HD2022 True   <df [21 × 7]> <named list [12]> <named list [1]>  # Using CalmrExperiment, for more iterations df <- get_design(\"blocking\") pars <- get_parameters(df, model = \"SM2007\") exper <- make_experiment(df,   parameters = pars, model = \"SM2007\",   options = get_exp_opts(iterations = 4) ) run_experiment(exper) #> # A tibble: 8 × 6 #>   iteration model  group    experience    mapping           parameters       #>       <int> <chr>  <chr>    <list>        <list>            <list>           #> 1         1 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 2         1 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]> #> 3         2 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 4         2 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]> #> 5         3 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 6         3 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]> #> 7         4 SM2007 Blocking <df [21 × 7]> <named list [12]> <named list [7]> #> 8         4 SM2007 Control  <df [11 × 7]> <named list [12]> <named list [7]>"},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Set reward parameters for ANCCR model — set_reward_parameters","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"Set reward parameters ANCCR model","code":""},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"","code":"set_reward_parameters(parameters, rewards = c(\"US\"))"},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"parameters list parameters, returned get_parameters rewards character vector specifying reward stimuli. Default = c(\"US\")","code":""},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"list parameters","code":""},{"path":"https://victornavarro.org/calmr/reference/set_reward_parameters.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Set reward parameters for ANCCR model — set_reward_parameters","text":"default behaviour get_parameters ANCCR model set every reward-related parameter non-zero default value. function set parameters zero non-reward stimuli","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-051","dir":"Changelog","previous_headings":"","what":"calmr 0.5.1","title":"calmr 0.5.1","text":"Added parallelization progress bars via future, future.apply, progressr. Function calmr_verbosity can set verbosity package.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-050","dir":"Changelog","previous_headings":"","what":"calmr 0.5.0","title":"calmr 0.5.0","text":"Implementation ANCCR (Jeong et al., 2022), first time-based model included calmr. Added parameter distinction trial-wise period-wise parameters. Added internal augmentation design/arguments depending model. trial-based models use pre/post distinctions anymore. Using “>” special character affect models anymore. “>” special character used specify periods within trial. example, “>B>C” implies followed B followed C. See “using_time_models” vignette additional information. Named stimuli now support numbers trailing characters (e.g., “(US1)” valid now.)","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-040","dir":"Changelog","previous_headings":"","what":"calmr 0.4.0","title":"calmr 0.4.0","text":"Major refactoring classes models. help development moving forward. Added several methods access CalmrExperiment contents, including c (bind experiments) results, plot, graph, design parameters. Created CalmrDesign CalmrResult classes. Rewrote parsers less verbose rely less tidyverse suite piping. Substantially reduced complexity make_experiment function (previous make_model_args). Introduced distinction stimulus-specific global parameters. Parameters now lists instead data.frames. Modified UI calmr app include sidebar (reduce clutter). Simplified app removing options. Nearly duplicated number tests.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-030","dir":"Changelog","previous_headings":"","what":"calmr 0.3.0","title":"calmr 0.3.0","text":"Added first version SOCR model (SM2007) well two vignettes explaining math behind implementation quick simulations. Warning: EXPERIMENTAL. Documentation progress.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-020","dir":"Changelog","previous_headings":"","what":"calmr 0.2.0","title":"calmr 0.2.0","text":"Added multiple models package app (RW1972, PKH1982, MAC1975). Implementation basic S4 classes model, experiment, fit, RSA comparison objects, well methods. Added genetic algorithms (via GA) parameter estimation. Added basic tools perform representational similarity analysis. Documentation progress.","code":""},{"path":"https://victornavarro.org/calmr/news/index.html","id":"calmr-010","dir":"Changelog","previous_headings":"","what":"calmr 0.1.0","title":"calmr 0.1.0","text":"heidi now calmr: Canonical Associative Learning Models R. package now aims maintain several associative learning models implement tools use. Major overhaul training function (train_pav_model). relevant calculations now done function functional stimuli instead just US. Support specification expectation/correction steps within trial via “>”. example, trial “>(US)” use generate expectation, learn stimuli correction step. previous plotting function R-values revamped allow simple complex versions. complex version facets r-values predictor basis, uses colour lines target. Bugfix related stimulus saliencies.","code":""}]