From 50ee454c52848194884bac263aba7387947e9d83 Mon Sep 17 00:00:00 2001 From: mehmet umut caglar Date: Fri, 17 Mar 2017 12:23:01 -0500 Subject: [PATCH] some updates and fixes --- .DS_Store | Bin 8196 -> 8196 bytes DESCRIPTION | 2 +- R/categorize.R | 4 +- R/doublesigmoidalFitFunctions.R | 2 +- Readme.md | 2 +- inst/doc/Introduction.html | 4 +- inst/doc/double_sigmoidal_vignette.R | 98 + inst/doc/double_sigmoidal_vignette.Rmd | 241 + inst/doc/double_sigmoidal_vignette.html | 321 + inst/doc/linear_vignette.R | 27 +- inst/doc/linear_vignette.Rmd | 41 +- inst/doc/linear_vignette.html | 40 +- inst/doc/sigmoidal_vignette.R | 34 +- inst/doc/sigmoidal_vignette.Rmd | 97 +- inst/doc/sigmoidal_vignette.html | 85 +- man/categorize.Rd | 3 +- man/categorize_nosignal.Rd | 2 +- .../candidate functions with explanations.m | 394 - .../candidate functions with explanations.nb | 10401 ---------------- mathematica/new candidate functions.nb | 3524 ------ mathematica/presentation.m | 350 - mathematica/simple toy model.nb | 229 - vignettes/double_sigmoidal_vignette.Rmd | 65 +- vignettes/linear_vignette.Rmd | 29 +- vignettes/sigmoidal_vignette.Rmd | 43 +- 25 files changed, 901 insertions(+), 15137 deletions(-) create mode 100644 inst/doc/double_sigmoidal_vignette.R create mode 100644 inst/doc/double_sigmoidal_vignette.Rmd create mode 100644 inst/doc/double_sigmoidal_vignette.html delete mode 100644 mathematica/candidate functions with explanations.m delete mode 100644 mathematica/candidate functions with explanations.nb delete mode 100644 mathematica/new candidate functions.nb delete mode 100644 mathematica/presentation.m delete mode 100644 mathematica/simple toy model.nb diff --git a/.DS_Store b/.DS_Store index f14e9e8c91a1f1f82966683f5598a3711895e997..f8e712a005b8fc82d0ffcc2f33a95ebf1aa5d87d 100644 GIT binary patch delta 40 wcmZp1XmOa}UDU^hRb;$|L!c&5#k!sl2f7M$D6F7b_J^J!5Vrilfo0U8PpumAu6 delta 227 zcmZp1XmOa}OBU^hRb+GZYsc&2)8hFpe3h7yJhhEymklOdTQF(=(HI5|JJfB^)! z3b=qI4wbq2E-ophCCLm77kQN~fz{(wnS!Cx0lW1D8OYWz0GW!z(9JPIcUd;GOMGM5 JtS`dO3;+T{GZp{< diff --git a/DESCRIPTION b/DESCRIPTION index bb1e10a..c48344e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: sicegar Type: Package Title: Analysis of Single-Cell Viral Growth Curves -Version: 0.1 +Version: 0.1.0.0000 Date: 2015-12-17 Authors@R: c( person("M. Umut", "Caglar", role = c("aut", "cre"), email = "umut.caglar@gmail.com"), person("Claus O.", "Wilke", role = c("aut"), email = diff --git a/R/categorize.R b/R/categorize.R index afbbdfb..a2f0054 100644 --- a/R/categorize.R +++ b/R/categorize.R @@ -186,7 +186,7 @@ categorize<- parameterVectorDoubleSigmoidal, threshold_line_slope_parameter=0.01, threshold_intensity_interval=0.1, - threshold_minimum_for_intensity_maximum=0.4, + threshold_minimum_for_intensity_maximum=0.3, threshold_difference_AIC=0, threshold_lysis_finalAsymptoteIntensity=0.75, threshold_AIC=-10) @@ -376,7 +376,7 @@ categorize_nosignal<- function(parameterVectorLinear, threshold_line_slope_parameter=0.01, threshold_intensity_interval=0.1, - threshold_minimum_for_intensity_maximum=0.4) + threshold_minimum_for_intensity_maximum=0.3) { #************************************************ # First Part Define NA diff --git a/R/doublesigmoidalFitFunctions.R b/R/doublesigmoidalFitFunctions.R index 600f8e0..9a2209e 100644 --- a/R/doublesigmoidalFitFunctions.R +++ b/R/doublesigmoidalFitFunctions.R @@ -475,7 +475,7 @@ f_mid2_doublesigmoidal <- function(parameterDf){ B2=parameterDf$slope2_Estimate , L=parameterDf$midPointDistance_Estimate); - mid2x <- stats::uniroot(f0mid, interval=c(argumentt,max_x*(3)), + mid2x <- stats::uniroot(f0mid, interval=c(argumentt,max_x*(2)), tol=0.0001, B1=parameterDf$slope1_Estimate, M1=parameterDf$midPoint1_Estimate, diff --git a/Readme.md b/Readme.md index e8c7564..425cd14 100644 --- a/Readme.md +++ b/Readme.md @@ -2,4 +2,4 @@ Package for single-cell virology. -We need some more description here, small change v2. +the package aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal and double sigmoidal curves on to time vs intensity data. The all the fits together used to make decision between sigmoidal, double sigmoidal, no signal or ambiguous. No signal means the intensity do not reach to a high enough point or do not change at all. Sigmoidal means intensity start from a small number than climb to a maximum. Double sigmoidal means intensity start from a small number, climb to a maximum then starts to decay. After the decision between those four options, algorithm gives sigmoidal (or double sigmoidal) associated parameter values that quantifies the time intensity curve. diff --git a/inst/doc/Introduction.html b/inst/doc/Introduction.html index 12f8cb4..5aa500c 100644 --- a/inst/doc/Introduction.html +++ b/inst/doc/Introduction.html @@ -12,7 +12,7 @@ - + Vignette Title @@ -70,7 +70,7 @@

Vignette Title

Vignette Author

-

2017-03-10

+

2017-03-15

diff --git a/inst/doc/double_sigmoidal_vignette.R b/inst/doc/double_sigmoidal_vignette.R new file mode 100644 index 0000000..36323d0 --- /dev/null +++ b/inst/doc/double_sigmoidal_vignette.R @@ -0,0 +1,98 @@ +## ----setup, include=FALSE------------------------------------------------ +knitr::opts_chunk$set(echo = TRUE) + +## ----install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE---- + +###***************************** +# INITIAL COMMANDS TO RESET THE SYSTEM +rm(list = ls()) +if (is.integer(dev.list())){dev.off()} +cat("\014") +seedNo=14159 +set.seed(seedNo) +###***************************** + +###***************************** +require("sicegar") +require("dplyr") +require("ggplot2") +###***************************** + +## ----generate data------------------------------------------------------- +time=seq(3,24,0.5) + +#simulate intensity data and add noise +noise_parameter=0.1 +intensity_noise=stats::runif(n = length(time),min = 0,max = 1)*noise_parameter +intensity=doublesigmoidalFitFormula(time, + finalAsymptoteIntensity=.3, + maximum=4, + slope1=1, + midPoint1=7, + slope2=1, + midPointDistance=8) +intensity=intensity+intensity_noise + +dataInput=data.frame(intensity=intensity,time=time) + +## ----time normalization, eval=FALSE-------------------------------------- +# timeRatio=max(timeData); timeData=timeData/timeRatio + +## ----intensity normalization, eval=FALSE--------------------------------- +# intensityMin = min(dataInput$intensity) +# intensityMax = max(dataInput$intensity) +# intensityRatio = intensityMax - intensityMin +# +# intensityData=dataInput$intensity-intensityMin +# intensityData=intensityData/intensityRatio + +## ----normalize_data------------------------------------------------------ +normalizedInput = sicegar::normalizeData(dataInput = dataInput, + dataInputName = "Sample001") + +## ----normalized_data_output---------------------------------------------- +head(normalizedInput$timeIntensityData) # the normalized time and intensity data +print(normalizedInput$dataScalingParameters) # the normalization parameters that is needed to go back to original scale +print(normalizedInput$dataInputName) # a useful feature to track the sample in all the process + +## ----plot raw and normal data, echo=FALSE, fig.height=4, fig.width=8----- +dataInput %>% dplyr::mutate(process="raw")->dataInput2 +normalizedInput$timeIntensityData %>% + dplyr::mutate(process="normalized")->timeIntensityData2 +dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined +combined$process <- factor(combined$process, levels = c("raw","normalized")) + +ggplot2::ggplot(combined,aes(x=time, y=intensity))+ + ggplot2::facet_wrap(~process, scales = "free")+ + ggplot2::geom_point() + +## ----doublesigmoidalfit_data--------------------------------------------- +parameterVector<-sicegar::doublesigmoidalFitFunction(normalizedInput,tryCounter=2) + +# Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. + +# If tryCounter==1 it took the start position given by sicegar::fitFunction +# If tryCounter!=1 it generates a random start position from given interval + +## ----parameter vector---------------------------------------------------- +print(t(parameterVector)) + +## ----plot raw data and fit, fig.height=4, fig.width=8-------------------- +intensityTheoretical= + sicegar::doublesigmoidalFitFormula( + time, + finalAsymptoteIntensity=parameterVector$finalAsymptoteIntensity_Estimate, + maximum=parameterVector$maximum_Estimate, + slope1=parameterVector$slope1_Estimate, + midPoint1=parameterVector$midPoint1_Estimate, + slope2=parameterVector$slope2_Estimate, + midPointDistance=parameterVector$midPointDistance_Estimate) + +comparisonData=cbind(dataInput,intensityTheoretical) + +require(ggplot2) +ggplot2::ggplot(comparisonData)+ + ggplot2::geom_point(aes(x=time, y=intensity))+ + ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+ + ggplot2::expand_limits(x = 0, y = 0) + diff --git a/inst/doc/double_sigmoidal_vignette.Rmd b/inst/doc/double_sigmoidal_vignette.Rmd new file mode 100644 index 0000000..b885070 --- /dev/null +++ b/inst/doc/double_sigmoidal_vignette.Rmd @@ -0,0 +1,241 @@ +--- +title: (3/3) Double Sigmoidal model +author: "Umut Caglar" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Vignette Title} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +# The Double Sigmoidal Fit Function +This is a document invetigates details of double sigmoidal model + + +```{r install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE} + +###***************************** +# INITIAL COMMANDS TO RESET THE SYSTEM +rm(list = ls()) +if (is.integer(dev.list())){dev.off()} +cat("\014") +seedNo=14159 +set.seed(seedNo) +###***************************** + +###***************************** +require("sicegar") +require("dplyr") +require("ggplot2") +###***************************** +``` + +## Data generation +To simulate the results, we will go backwards and firstly generate some data to analize. To add some randomness to the input data I will use some noise. The input of all package must be in the form of a data frame with at least 2 columns time and intensity. + +`sicegar::doublesigmoidalFitFormula` generate a set of intensity values based on finalAsymptoteIntensity, maximum, slope1, midPoint1,slope2, midPointDistance values supplied. So here we are generating a set of points that are on two sigmoidals glued side by side from the maximum of the combined function. + +note that slope1, slope2 and finalAsymptoteIntensity are not exactly the exacly slope1 slope2 and final asymtote intensity of the final function. they are parameters related with them. + +```{r generate data} +time=seq(3,24,0.5) + +#simulate intensity data and add noise +noise_parameter=0.1 +intensity_noise=stats::runif(n = length(time),min = 0,max = 1)*noise_parameter +intensity=doublesigmoidalFitFormula(time, + finalAsymptoteIntensity=.3, + maximum=4, + slope1=1, + midPoint1=7, + slope2=1, + midPointDistance=8) +intensity=intensity+intensity_noise + +dataInput=data.frame(intensity=intensity,time=time) +``` + +## Data normalization + +This is the first step. Data should be normalized before any fit. I.e time and intensity should be in between 0-1 interval. + +There is a nuance + +* Time is normalized with respect to maximum value the time parameter takes. +```{r time normalization, eval=FALSE} +timeRatio=max(timeData); timeData=timeData/timeRatio +``` + +* Intensity is normalized with respect to intensity interval +```{r intensity normalization, eval=FALSE} +intensityMin = min(dataInput$intensity) +intensityMax = max(dataInput$intensity) +intensityRatio = intensityMax - intensityMin + +intensityData=dataInput$intensity-intensityMin +intensityData=intensityData/intensityRatio +``` + +The normalization code is + +```{r normalize_data} +normalizedInput = sicegar::normalizeData(dataInput = dataInput, + dataInputName = "Sample001") +``` + + +Components of the normalization output + +```{r normalized_data_output} +head(normalizedInput$timeIntensityData) # the normalized time and intensity data +print(normalizedInput$dataScalingParameters) # the normalization parameters that is needed to go back to original scale +print(normalizedInput$dataInputName) # a useful feature to track the sample in all the process +``` + + +## The figures of raw and normalized datasets + +```{r plot raw and normal data, echo=FALSE, fig.height=4, fig.width=8} +dataInput %>% dplyr::mutate(process="raw")->dataInput2 +normalizedInput$timeIntensityData %>% + dplyr::mutate(process="normalized")->timeIntensityData2 +dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined +combined$process <- factor(combined$process, levels = c("raw","normalized")) + +ggplot2::ggplot(combined,aes(x=time, y=intensity))+ + ggplot2::facet_wrap(~process, scales = "free")+ + ggplot2::geom_point() +``` + +## Double-Sigmoidal fit of the data + +Now it is time to calculate the parameters by using `sicegar::doublesigmoidalFitFunction()` + +```{r doublesigmoidalfit_data} +parameterVector<-sicegar::doublesigmoidalFitFunction(normalizedInput,tryCounter=2) + +# Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. + +# If tryCounter==1 it took the start position given by sicegar::fitFunction +# If tryCounter!=1 it generates a random start position from given interval +``` + +the function outputs a vector that gives information about multiple parameters + +```{r parameter vector} +print(t(parameterVector)) +``` + +Here is the brief explanations of the parameters that are given by `sicegar::doublesigmoidalFitFunction` (In different order then than the output vector of the doublesigmoidalFitFunction) + +These are the parameters of the normalization step + +* `dataScalingParameters.timeRatio`: Maximum of raw time data +* `dataScalingParameters.intensityMin`: Minimum of raw intensity data +* `dataScalingParameters.intensityMax`: Maximum of raw intensity data +* `dataScalingParameters.intensityRatio`: Maximum - Minimum of intensity data + +They are the meta summary of the result parameters + +* `isThisaFit` +* `model` +* `numericalParameters`: __"FALSE"__ in this case + +Likelihood maximization algorithm starts from a random initiation (if tryCounter!=1) point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm. + +* `startVector.maximum`: maximum value of initiation point +* `startVector.slope1`: slope1 value of initiation point +* `startVector.midPoint1`: midPoint1 value of initiation point +* `startVector.slope2`: slope2 value of initiation point +* `startVector.midPointDistance`: midPointDistance value of initiation point +* `startVector.finalAsymptoteIntensity`: finalAsymptoteIntensity value of initiation point. + +For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter + +They are the parameters associated with parameter “maximum” + +* `maximum_N_Estimate`: Here N stand for the intersection in the normalized scale +* `maximum_Std_Error` +* `maximum_t_value` +* `maximum_Pr_t` + +They are the parameters associated with parameter “slope1” + +* `slope1_N_Estimate`: Here N stand for the intersection in the normalized scale +* `slope1_Std_Error` +* `slope1_t_value` +* `slope1_Pr_t` + +They are the parameters associated with parameter “midPoint1” + +* `midPoint1_N_Estimate`: Here N stand for the intersection in the normalized scale +* `midPoint1_Std_Error` +* `midPoint1_t_value` +* `midPoint1_Pr_t` + +They are the parameters associated with parameter “slope2” + +* `slope2_N_Estimate`: Here N stand for the intersection in the normalized scale +* `slope2_Std_Error` +* `slope2_t_value` +* `slope2_Pr_t` + + +They are the parameters associated with parameter “midPointDistance” + +* `midPointDistance_N_Estimate`: Here N stand for the intersection in the normalized scale +* `midPointDistance_Std_Error` +* `midPointDistance_t_value` +* `midPointDistance_Pr_t` + +They are the parameters associated with parameter “finalAsymptoteIntensity” + +* `finalAsymptoteIntensity_N_Estimate`: Here N stand for the intersection in the normalized scale +* `finalAsymptoteIntensity_Std_Error` +* `finalAsymptoteIntensity_t_value` +* `finalAsymptoteIntensity_Pr_t` + +They are the parameters associated with the quality of the fit. + +* `residual_Sum_of_Squares`: Small value indicate better fit +* `log_likelihood`: Higher value indicate a better fit +* `AIC_value`: Smaller value indicate a better fit +* `BIC_value`: Smaller value indicate a better fit + +They are the fitted values after converting everything from normalized to un-normalized scale. (Without numeric correction) + +* `maximum_Estimate`: Maximum intensity estimate for the raw data +* `slope1_Estimate`: __Slope1 parameter__ estimate for the raw data +* `midPoint1_Estimate`: Mid-point 1 estimate (time the intensity reaches 1/2 of maximum) for the raw data. _Needs numerical correction_ +* `slope2_Estimate`: __Slope2 parameter__ estimate for the raw data +* `midPointDistance_Estimate`: Distance between mid- point 1 and mid-point 2. Where mid-point 2 is the time that intensity decreases to the value in between final asymptote intensity and maximum value. _Needs numerical correction_ +* `finalAsymptoteIntensity_Estimate`: This is the __ratio__ between asymptote intensity and maximum value of the function. + +## Check the results to see if the results are meaningfull + +By using the `maximum_Estimate`, `slope_Estimate`, `midPoint_Estimate` parameters of the sigmoidalfit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::sigmoidalFitFormula()`. We can draw the best sigmoidal fit on top of our initial data. + +```{r plot raw data and fit, fig.height=4, fig.width=8} +intensityTheoretical= + sicegar::doublesigmoidalFitFormula( + time, + finalAsymptoteIntensity=parameterVector$finalAsymptoteIntensity_Estimate, + maximum=parameterVector$maximum_Estimate, + slope1=parameterVector$slope1_Estimate, + midPoint1=parameterVector$midPoint1_Estimate, + slope2=parameterVector$slope2_Estimate, + midPointDistance=parameterVector$midPointDistance_Estimate) + +comparisonData=cbind(dataInput,intensityTheoretical) + +require(ggplot2) +ggplot2::ggplot(comparisonData)+ + ggplot2::geom_point(aes(x=time, y=intensity))+ + ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+ + ggplot2::expand_limits(x = 0, y = 0) +``` diff --git a/inst/doc/double_sigmoidal_vignette.html b/inst/doc/double_sigmoidal_vignette.html new file mode 100644 index 0000000..2cd09e5 --- /dev/null +++ b/inst/doc/double_sigmoidal_vignette.html @@ -0,0 +1,321 @@ + + + + + + + + + + + + + + + + +(3/3) Double Sigmoidal model + + + + + + + + + + + + + + + + + +

(3/3) Double Sigmoidal model

+

Umut Caglar

+

2017-03-15

+ + + +
+

The Double Sigmoidal Fit Function

+

This is a document invetigates details of double sigmoidal model

+
+

Data generation

+

To simulate the results, we will go backwards and firstly generate some data to analize. To add some randomness to the input data I will use some noise. The input of all package must be in the form of a data frame with at least 2 columns time and intensity.

+

sicegar::doublesigmoidalFitFormula generate a set of intensity values based on finalAsymptoteIntensity, maximum, slope1, midPoint1,slope2, midPointDistance values supplied. So here we are generating a set of points that are on two sigmoidals glued side by side from the maximum of the combined function.

+

note that slope1, slope2 and finalAsymptoteIntensity are not exactly the exacly slope1 slope2 and final asymtote intensity of the final function. they are parameters related with them.

+
time=seq(3,24,0.5)
+
+#simulate intensity data and add noise
+noise_parameter=0.1
+intensity_noise=stats::runif(n = length(time),min = 0,max = 1)*noise_parameter
+intensity=doublesigmoidalFitFormula(time,
+                                    finalAsymptoteIntensity=.3,
+                                    maximum=4,
+                                    slope1=1,
+                                    midPoint1=7,
+                                    slope2=1,
+                                    midPointDistance=8)
+intensity=intensity+intensity_noise
+
+dataInput=data.frame(intensity=intensity,time=time)
+
+
+

Data normalization

+

This is the first step. Data should be normalized before any fit. I.e time and intensity should be in between 0-1 interval.

+

There is a nuance

+
    +
  • Time is normalized with respect to maximum value the time parameter takes.
    • +
+
timeRatio=max(timeData); timeData=timeData/timeRatio
+
    +
  • Intensity is normalized with respect to intensity interval
    • +
+
intensityMin = min(dataInput$intensity)
+intensityMax = max(dataInput$intensity)
+intensityRatio = intensityMax - intensityMin
+
+intensityData=dataInput$intensity-intensityMin
+intensityData=intensityData/intensityRatio
+

The normalization code is

+
normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
+

Components of the normalization output

+
head(normalizedInput$timeIntensityData) # the normalized time and intensity data
+
##        time   intensity
+## 1 0.1250000 0.000000000
+## 2 0.1458333 0.005575769
+## 3 0.1666667 0.014948772
+## 4 0.1875000 0.049302656
+## 5 0.2083333 0.092288619
+## 6 0.2291667 0.163302439
+
print(normalizedInput$dataScalingParameters) # the normalization parameters that is needed to go back to original scale
+
##      timeRatio   intensityMin   intensityMax intensityRatio 
+##     24.0000000      0.1455394      4.0735994      3.9280600
+
print(normalizedInput$dataInputName) # a useful feature to track the sample in all the process
+
## [1] "Sample001"
+
+
+

The figures of raw and normalized datasets

+

+
+
+

Double-Sigmoidal fit of the data

+

Now it is time to calculate the parameters by using sicegar::doublesigmoidalFitFunction()

+
parameterVector<-sicegar::doublesigmoidalFitFunction(normalizedInput,tryCounter=2)
+
+# Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
+
+# If tryCounter==1 it took the  start position given by sicegar::fitFunction
+# If tryCounter!=1 it generates a random start position from given interval
+

the function outputs a vector that gives information about multiple parameters

+
print(t(parameterVector))
+
##                                      [,1]             
+## finalAsymptoteIntensity_N_Estimate   "0"              
+## finalAsymptoteIntensity_Std_Error    "112.4602"       
+## finalAsymptoteIntensity_t_value      "0"              
+## finalAsymptoteIntensity_Pr_t         "1"              
+## maximum_N_Estimate                   "0.5579852"      
+## maximum_Std_Error                    "0.09694097"     
+## maximum_t_value                      "5.755928"       
+## maximum_Pr_t                         "1.344264e-06"   
+## slope1_N_Estimate                    "1.304416"       
+## slope1_Std_Error                     "229.1623"       
+## slope1_t_value                       "0.005692108"    
+## slope1_Pr_t                          "0.995489"       
+## midPoint1_N_Estimate                 "0.5323434"      
+## midPoint1_Std_Error                  "642.7553"       
+## midPoint1_t_value                    "0.0008282209"   
+## midPoint1_Pr_t                       "0.9993436"      
+## slope2_N_Estimate                    "2.928598"       
+## slope2_Std_Error                     "487.1463"       
+## slope2_t_value                       "0.006011743"    
+## slope2_Pr_t                          "0.9952356"      
+## midPointDistance_N_Estimate          "0.625"          
+## midPointDistance_Std_Error           "762.6677"       
+## midPointDistance_t_value             "0.0008194919"   
+## midPointDistance_Pr_t                "0.9993505"      
+## residual_Sum_of_Squares              "4.087576"       
+## log_likelihood                       "-10.41952"      
+## AIC_value                            "34.83905"       
+## BIC_value                            "47.16745"       
+## isThisaFit                           "TRUE"           
+## startVector.finalAsymptoteIntensity  "0.5251236"      
+## startVector.maximum                  "0.9545571"      
+## startVector.slope1                   "117.0177"       
+## startVector.midPoint1                "0.5727166"      
+## startVector.slope2                   "62.71368"       
+## startVector.midPointDistance         "0.4960475"      
+## dataScalingParameters.timeRatio      "24"             
+## dataScalingParameters.intensityMin   "0.1455394"      
+## dataScalingParameters.intensityMax   "4.073599"       
+## dataScalingParameters.intensityRatio "3.92806"        
+## model                                "doublesigmoidal"
+## numericalParameters                  "FALSE"          
+## finalAsymptoteIntensity_Estimate     "0"              
+## maximum_Estimate                     "2.337339"       
+## slope1_Estimate                      "0.05435069"     
+## midPoint1_Estimate                   "12.77624"       
+## slope2_Estimate                      "0.1220249"      
+## midPointDistance_Estimate            "15"
+

Here is the brief explanations of the parameters that are given by sicegar::doublesigmoidalFitFunction (In different order then than the output vector of the doublesigmoidalFitFunction)

+

These are the parameters of the normalization step

+
    +
  • dataScalingParameters.timeRatio: Maximum of raw time data
    +
    • +
  • dataScalingParameters.intensityMin: Minimum of raw intensity data
    • +
  • dataScalingParameters.intensityMax: Maximum of raw intensity data
    • +
  • dataScalingParameters.intensityRatio: Maximum - Minimum of intensity data
    • +
+

They are the meta summary of the result parameters

+
    +
  • isThisaFit
    • +
  • model
    • +
  • numericalParameters: “FALSE” in this case
    • +
+

Likelihood maximization algorithm starts from a random initiation (if tryCounter!=1) point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm.

+
    +
  • startVector.maximum: maximum value of initiation point
    • +
  • startVector.slope1: slope1 value of initiation point
    • +
  • startVector.midPoint1: midPoint1 value of initiation point
    • +
  • startVector.slope2: slope2 value of initiation point
    • +
  • startVector.midPointDistance: midPointDistance value of initiation point
    • +
  • startVector.finalAsymptoteIntensity: finalAsymptoteIntensity value of initiation point.
    • +
+

For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter

+

They are the parameters associated with parameter “maximum”

+
    +
  • maximum_N_Estimate: Here N stand for the intersection in the normalized scale
    • +
  • maximum_Std_Error
    • +
  • maximum_t_value
    • +
  • maximum_Pr_t
    • +
+

They are the parameters associated with parameter “slope1”

+
    +
  • slope1_N_Estimate: Here N stand for the intersection in the normalized scale
    • +
  • slope1_Std_Error
    • +
  • slope1_t_value
    • +
  • slope1_Pr_t
    • +
+

They are the parameters associated with parameter “midPoint1”

+
    +
  • midPoint1_N_Estimate: Here N stand for the intersection in the normalized scale
    • +
  • midPoint1_Std_Error
    • +
  • midPoint1_t_value
    • +
  • midPoint1_Pr_t
    • +
+

They are the parameters associated with parameter “slope2”

+
    +
  • slope2_N_Estimate: Here N stand for the intersection in the normalized scale
    • +
  • slope2_Std_Error
    • +
  • slope2_t_value
    • +
  • slope2_Pr_t
    • +
+

They are the parameters associated with parameter “midPointDistance”

+
    +
  • midPointDistance_N_Estimate: Here N stand for the intersection in the normalized scale
    • +
  • midPointDistance_Std_Error
    • +
  • midPointDistance_t_value
    • +
  • midPointDistance_Pr_t
    • +
+

They are the parameters associated with parameter “finalAsymptoteIntensity”

+
    +
  • finalAsymptoteIntensity_N_Estimate: Here N stand for the intersection in the normalized scale
    +
    • +
  • finalAsymptoteIntensity_Std_Error
    • +
  • finalAsymptoteIntensity_t_value
    • +
  • finalAsymptoteIntensity_Pr_t
    • +
+

They are the parameters associated with the quality of the fit.

+
    +
  • residual_Sum_of_Squares: Small value indicate better fit
    • +
  • log_likelihood: Higher value indicate a better fit
    • +
  • AIC_value: Smaller value indicate a better fit
    • +
  • BIC_value: Smaller value indicate a better fit
    • +
+

They are the fitted values after converting everything from normalized to un-normalized scale. (Without numeric correction)

+
    +
  • maximum_Estimate: Maximum intensity estimate for the raw data
    • +
  • slope1_Estimate: Slope1 parameter estimate for the raw data
    • +
  • midPoint1_Estimate: Mid-point 1 estimate (time the intensity reaches 1/2 of maximum) for the raw data. Needs numerical correction
    • +
  • slope2_Estimate: Slope2 parameter estimate for the raw data
    • +
  • midPointDistance_Estimate: Distance between mid- point 1 and mid-point 2. Where mid-point 2 is the time that intensity decreases to the value in between final asymptote intensity and maximum value. Needs numerical correction
    • +
  • finalAsymptoteIntensity_Estimate: This is the ratio between asymptote intensity and maximum value of the function.
    • +
+
+
+

Check the results to see if the results are meaningfull

+

By using the maximum_Estimate, slope_Estimate, midPoint_Estimate parameters of the sigmoidalfit and the time sequence that we already created we can calculate the intensity values by the help of sicegar::sigmoidalFitFormula(). We can draw the best sigmoidal fit on top of our initial data.

+
intensityTheoretical=
+  sicegar::doublesigmoidalFitFormula(
+    time,
+    finalAsymptoteIntensity=parameterVector$finalAsymptoteIntensity_Estimate,
+    maximum=parameterVector$maximum_Estimate,
+    slope1=parameterVector$slope1_Estimate,
+    midPoint1=parameterVector$midPoint1_Estimate,
+    slope2=parameterVector$slope2_Estimate,
+    midPointDistance=parameterVector$midPointDistance_Estimate)
+
+comparisonData=cbind(dataInput,intensityTheoretical)
+
+require(ggplot2)
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
+

+
+
+ + + + + + + + diff --git a/inst/doc/linear_vignette.R b/inst/doc/linear_vignette.R index b80ddd1..e3b39ab 100644 --- a/inst/doc/linear_vignette.R +++ b/inst/doc/linear_vignette.R @@ -16,7 +16,6 @@ set.seed(seedNo) require("sicegar") require("dplyr") require("ggplot2") -require("cowplot") ###***************************** ## ----generate data------------------------------------------------------- @@ -42,8 +41,8 @@ dataInput=data.frame(intensity=intensity,time=time) # intensityData=intensityData/intensityRatio ## ----normalize_data------------------------------------------------------ -normalizedInput = normalizeData(dataInput = dataInput, - dataInputName = "Sample001") +normalizedInput = sicegar::normalizeData(dataInput = dataInput, + dataInputName = "Sample001") ## ----normalized_data_output---------------------------------------------- head(normalizedInput$timeIntensityData) # the normalized time and intensity data @@ -57,12 +56,12 @@ normalizedInput$timeIntensityData %>% dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined combined$process <- factor(combined$process, levels = c("raw","normalized")) -ggplot(combined,aes(x=time, y=intensity))+ - facet_wrap(~process, scales = "free")+ - geom_point() +ggplot2::ggplot(combined,aes(x=time, y=intensity))+ + ggplot2::facet_wrap(~process, scales = "free")+ + ggplot2::geom_point() ## ----linefit_data-------------------------------------------------------- -parameterVector<-lineFitFunction(dataInput = normalizedInput, tryCounter = 2) +parameterVector<-sicegar::lineFitFunction(dataInput = normalizedInput, tryCounter = 2) # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::lineFitFunction is called from sicegar::fitFunction. @@ -73,13 +72,13 @@ parameterVector<-lineFitFunction(dataInput = normalizedInput, tryCounter = 2) print(t(parameterVector)) ## ----plot raw data and fit, fig.height=4, fig.width=8-------------------- -intensityTheoretical=lineFitFormula(time, - slope=parameterVector$slope_Estimate, - intersection=parameterVector$intersection_Estimate) +intensityTheoretical=sicegar::lineFitFormula(time, + slope=parameterVector$slope_Estimate, + intersection=parameterVector$intersection_Estimate) comparisonData=cbind(dataInput,intensityTheoretical) -ggplot(comparisonData)+ - geom_point(aes(x=time, y=intensity))+ - geom_line(aes(x=time,y=intensityTheoretical))+ - expand_limits(x = 0, y = 0) +ggplot2::ggplot(comparisonData)+ + ggplot2::geom_point(aes(x=time, y=intensity))+ + ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+ + ggplot2::expand_limits(x = 0, y = 0) diff --git a/inst/doc/linear_vignette.Rmd b/inst/doc/linear_vignette.Rmd index 90da925..66dc47c 100644 --- a/inst/doc/linear_vignette.Rmd +++ b/inst/doc/linear_vignette.Rmd @@ -32,7 +32,6 @@ set.seed(seedNo) require("sicegar") require("dplyr") require("ggplot2") -require("cowplot") ###***************************** ``` @@ -77,8 +76,8 @@ intensityData=intensityData/intensityRatio The normalization code is ```{r normalize_data} -normalizedInput = normalizeData(dataInput = dataInput, - dataInputName = "Sample001") +normalizedInput = sicegar::normalizeData(dataInput = dataInput, + dataInputName = "Sample001") ``` @@ -100,9 +99,9 @@ normalizedInput$timeIntensityData %>% dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined combined$process <- factor(combined$process, levels = c("raw","normalized")) -ggplot(combined,aes(x=time, y=intensity))+ - facet_wrap(~process, scales = "free")+ - geom_point() +ggplot2::ggplot(combined,aes(x=time, y=intensity))+ + ggplot2::facet_wrap(~process, scales = "free")+ + ggplot2::geom_point() ``` ## Line fit of the data @@ -110,7 +109,7 @@ ggplot(combined,aes(x=time, y=intensity))+ Now it is time to calculate the parameters by using `sicegar::lineFitFunction()` ```{r linefit_data} -parameterVector<-lineFitFunction(dataInput = normalizedInput, tryCounter = 2) +parameterVector<-sicegar::lineFitFunction(dataInput = normalizedInput, tryCounter = 2) # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::lineFitFunction is called from sicegar::fitFunction. @@ -124,7 +123,7 @@ the function outputs a vector that gives information about multiple parameters print(t(parameterVector)) ``` -Here is the brief explanations of the parameters that are given by lineFitFunction (In different order then than the output vector of the lineFitFunction) +Here is the brief explanations of the parameters that are given by `sicegar::lineFitFunction` (In different order then than the output vector of the `sicegar::lineFitFunction`) These are the parameters of the normalization step: @@ -138,10 +137,10 @@ They are the meta summary of the result parameters * `model`: Gives the used model for fitting * `isThisaFit`: FALSE means there is not any successful fit. TRUE means there is at least one successful fit -Likelihood maximization algorithm starts from a random initiation point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm. +Likelihood maximization algorithm starts from a random initiation point (if tryCounter!=1) and goes down the fitness space by a gradient decent algorithm. These parameters represent the start point of the gradient decent algorithm. -* `startVector.slope`: Slope value of the initialtion point -* `startVector.intersection`: Intersection value of the initialtion point +* `startVector.slope`: Slope value of the initiation point +* `startVector.intersection`: Intersection value of the initiation point For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter. __Note: They are for normalized data.__ @@ -172,7 +171,7 @@ They are the parameters associated with the quality of the fit. Final results that are relavent to most of the users -They are the fitter values after converting everything from normalized to un-normalized scale. +They are the fitted values after converting everything from normalized to un-normalized scale. * `intersection_Estimate`: Intersection estimate for the raw data * `slope_Estimate`: Slope estimate for the raw data @@ -180,16 +179,16 @@ They are the fitter values after converting everything from normalized to un-nor ## Check the results to see if the results are meaningfull -By using the `intersection_Estimate`, `slope_Estimate` parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::lineFitFormula()`. We can draw the bes line on top of our initial data. +By using the `intersection_Estimate`, `slope_Estimate` parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::lineFitFormula()`. We can draw the best line on top of our initial data. ```{r plot raw data and fit, fig.height=4, fig.width=8} -intensityTheoretical=lineFitFormula(time, - slope=parameterVector$slope_Estimate, - intersection=parameterVector$intersection_Estimate) +intensityTheoretical=sicegar::lineFitFormula(time, + slope=parameterVector$slope_Estimate, + intersection=parameterVector$intersection_Estimate) comparisonData=cbind(dataInput,intensityTheoretical) -ggplot(comparisonData)+ - geom_point(aes(x=time, y=intensity))+ - geom_line(aes(x=time,y=intensityTheoretical))+ - expand_limits(x = 0, y = 0) -``` \ No newline at end of file +ggplot2::ggplot(comparisonData)+ + ggplot2::geom_point(aes(x=time, y=intensity))+ + ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+ + ggplot2::expand_limits(x = 0, y = 0) +``` diff --git a/inst/doc/linear_vignette.html b/inst/doc/linear_vignette.html index 00f11d1..dc634e4 100644 --- a/inst/doc/linear_vignette.html +++ b/inst/doc/linear_vignette.html @@ -12,7 +12,7 @@ - + (1/3) Linear model @@ -70,7 +70,7 @@

(1/3) Linear model

Umut Caglar

-

2017-03-10

+

2017-03-15

@@ -109,8 +109,8 @@

Data normalization

intensityData=dataInput$intensity-intensityMin intensityData=intensityData/intensityRatio

The normalization code is

-
normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
+
normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")

Components of the normalization output

head(normalizedInput$timeIntensityData) # the normalized time and intensity data
##        time  intensity
@@ -128,12 +128,12 @@ 
Data normalization

 
 

 
The figures of raw and normalized datasets

-

+

 

 

 
Line fit of the data

 
Now it is time to calculate the parameters by using sicegar::lineFitFunction()

-
parameterVector<-lineFitFunction(dataInput = normalizedInput, tryCounter = 2)
+
parameterVector<-sicegar::lineFitFunction(dataInput = normalizedInput, tryCounter = 2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::lineFitFunction is called from sicegar::fitFunction. 
 
@@ -164,7 +164,7 @@ 
Line fit of the data

 ## model                                "linaer"      
 ## intersection_Estimate                "1.448682"    
 ## slope_Estimate                       "4.022677"

-
Here is the brief explanations of the parameters that are given by lineFitFunction (In different order then than the output vector of the lineFitFunction)

+
Here is the brief explanations of the parameters that are given by sicegar::lineFitFunction (In different order then than the output vector of the sicegar::lineFitFunction)

 
These are the parameters of the normalization step:

 
    
 
  • dataScalingParameters.timeRatio: Maximum of raw time data
    
@@ -178,10 +178,10 @@ 
    Line fit of the data
    
 
  • model: Gives the used model for fitting
    • 
 
  • isThisaFit: FALSE means there is not any successful fit. TRUE means there is at least one successful fit
    • 
 

-
Likelihood maximization algorithm starts from a random initiation point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm.

+
Likelihood maximization algorithm starts from a random initiation point (if tryCounter!=1) and goes down the fitness space by a gradient decent algorithm. These parameters represent the start point of the gradient decent algorithm.

 
    
-
  • startVector.slope: Slope value of the initialtion point
    • 
-
  • startVector.intersection: Intersection value of the initialtion point
    • 
+
  • startVector.slope: Slope value of the initiation point
    • 
+
  • startVector.intersection: Intersection value of the initiation point
    • 
 

 
For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter. Note: They are for normalized data.

 
They are the parameters associated with parameter “slope”

@@ -210,7 +210,7 @@ 
Line fit of the data

 
  • BIC_value: Smaller value indicate a better fit
    • 
 
 
    Final results that are relavent to most of the users
    
-
    They are the fitter values after converting everything from normalized to un-normalized scale.
    
+
    They are the fitted values after converting everything from normalized to un-normalized scale.
    
 
      
 
    • intersection_Estimate: Intersection estimate for the raw data
      • 
 
    • slope_Estimate: Slope estimate for the raw data
      • 
@@ -218,17 +218,17 @@ 
      Line fit of the data
      
 
    
 
    
 
    Check the results to see if the results are meaningfull
    
-
    By using the intersection_Estimate, slope_Estimate parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of sicegar::lineFitFormula(). We can draw the bes line on top of our initial data.
    
-
    intensityTheoretical=lineFitFormula(time,
-                                    slope=parameterVector$slope_Estimate,
-                                    intersection=parameterVector$intersection_Estimate)
+
    By using the intersection_Estimate, slope_Estimate parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of sicegar::lineFitFormula(). We can draw the best line on top of our initial data.
    
+
    intensityTheoretical=sicegar::lineFitFormula(time,
+                                             slope=parameterVector$slope_Estimate,
+                                             intersection=parameterVector$intersection_Estimate)
 comparisonData=cbind(dataInput,intensityTheoretical)
 
-ggplot(comparisonData)+
-  geom_point(aes(x=time, y=intensity))+
-  geom_line(aes(x=time,y=intensityTheoretical))+
-  expand_limits(x = 0, y = 0)
    
-
    
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
    
+
    
 
    
 
    
 
diff --git a/inst/doc/sigmoidal_vignette.R b/inst/doc/sigmoidal_vignette.R
index 760950b..f7bdf22 100644
--- a/inst/doc/sigmoidal_vignette.R
+++ b/inst/doc/sigmoidal_vignette.R
@@ -16,7 +16,6 @@ set.seed(seedNo)
 require("sicegar")
 require("dplyr")
 require("ggplot2")
-require("cowplot")
 ###*****************************
 
 ## ----generate data-------------------------------------------------------
@@ -42,8 +41,8 @@ dataInput=data.frame(intensity=intensity,time=time)
 #  intensityData=intensityData/intensityRatio
 
 ## ----normalize_data------------------------------------------------------
-normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
+normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
 
 ## ----normalized_data_output----------------------------------------------
 head(normalizedInput$timeIntensityData) # the normalized time and intensity data
@@ -57,12 +56,12 @@ normalizedInput$timeIntensityData %>%
 dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined
 combined$process <- factor(combined$process, levels = c("raw","normalized"))
 
-ggplot(combined,aes(x=time, y=intensity))+
-  facet_wrap(~process, scales = "free")+
-  geom_point()
+ggplot2::ggplot(combined,aes(x=time, y=intensity))+
+  ggplot2::facet_wrap(~process, scales = "free")+
+  ggplot2::geom_point()
 
-## ----linefit_data--------------------------------------------------------
-parameterVector<-sigmoidalFitFunction(normalizedInput,tryCounter=2)
+## ----sigmoidalfit_data---------------------------------------------------
+parameterVector<-sicegar::sigmoidalFitFunction(normalizedInput,tryCounter=2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
 
@@ -73,13 +72,14 @@ parameterVector<-sigmoidalFitFunction(normalizedInput,tryCounter=2)
 print(t(parameterVector))
 
 ## ----plot raw data and fit, fig.height=4, fig.width=8--------------------
-# intensityTheoretical=lineFitFormula(time,
-#                                     slope=parameterVector$slope_Estimate,
-#                                     intersection=parameterVector$intersection_Estimate)
-# comparisonData=cbind(dataInput,intensityTheoretical)
-# 
-# ggplot(comparisonData)+
-#   geom_point(aes(x=time, y=intensity))+
-#   geom_line(aes(x=time,y=intensityTheoretical))+
-#   expand_limits(x = 0, y = 0)
+intensityTheoretical=sicegar::sigmoidalFitFormula(time,
+                                                  maximum=parameterVector$maximum_Estimate,
+                                                  slope=parameterVector$slope_Estimate,
+                                                  midPoint=parameterVector$midPoint_Estimate)
+comparisonData=cbind(dataInput,intensityTheoretical)
+
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
 
diff --git a/inst/doc/sigmoidal_vignette.Rmd b/inst/doc/sigmoidal_vignette.Rmd
index 5133438..25bcc18 100644
--- a/inst/doc/sigmoidal_vignette.Rmd
+++ b/inst/doc/sigmoidal_vignette.Rmd
@@ -12,12 +12,12 @@ vignette: >
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
-
-# The Sigmoidal Fit Function
-This is a document invetigates details of sigmoidal model 
-
-
-```{r install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE}
+  
+  # The Sigmoidal Fit Function
+  This is a document invetigates details of sigmoidal model 
+  
+  
+  ```{r install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE}
 
 ###*****************************
 # INITIAL COMMANDS TO RESET THE SYSTEM
@@ -32,7 +32,6 @@ set.seed(seedNo)
 require("sicegar")
 require("dplyr")
 require("ggplot2")
-require("cowplot")
 ###*****************************
 ```
 
@@ -83,8 +82,8 @@ intensityData=intensityData/intensityRatio
 The normalization code is 
 
 ```{r normalize_data}
-normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
+normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
 ```
 
 
@@ -106,17 +105,17 @@ normalizedInput$timeIntensityData %>%
 dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined
 combined$process <- factor(combined$process, levels = c("raw","normalized"))
 
-ggplot(combined,aes(x=time, y=intensity))+
-  facet_wrap(~process, scales = "free")+
-  geom_point()
+ggplot2::ggplot(combined,aes(x=time, y=intensity))+
+  ggplot2::facet_wrap(~process, scales = "free")+
+  ggplot2::geom_point()
 ```
 
 ## Sigmoidal fit of the data
 
 Now it is time to calculate the parameters by using `sicegar::sigmoidalFitFunction()`
 
-```{r linefit_data}
-parameterVector<-sigmoidalFitFunction(normalizedInput,tryCounter=2)
+```{r sigmoidalfit_data}
+parameterVector<-sicegar::sigmoidalFitFunction(normalizedInput,tryCounter=2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
 
@@ -130,9 +129,9 @@ the function outputs a vector that gives information about multiple parameters
 print(t(parameterVector))
 ```
 
-Here is the brief explanations of the parameters that are given by lineFitFunction (In different order then than the output vector of the lineFitFunction)
+Here is the brief explanations of the parameters that are given by `sicegar::sigmoidalFitFunction` (In different order then than the output vector of the sigmoidalFitFunction)
 
-These are the parameters of the normalization step:
+These are the parameters of the normalization step 
 
 * `dataScalingParameters.timeRatio`: Maximum of raw time data       
 * `dataScalingParameters.intensityMin`: Minimum of raw intensity data
@@ -144,30 +143,35 @@ They are the meta summary of the result parameters
 * `model`: Gives the used model for fitting
 * `isThisaFit`: FALSE means there is not any successful fit. TRUE means there is at least one successful fit 
 
-Likelihood maximization algorithm starts from a random initiation point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm.
+Likelihood maximization algorithm starts from a random initiation (if tryCounter!=1) point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm. 
+
+* `startVector.maximum`: Maximum value of the initiation point
+* `startVector.slope`: Slope value of the initiation point
+* `startVector.midPoint`: Mid-point value of the initiation point
 
-* `startVector.slope`: Slope value of the initialtion point
-* `startVector.intersection`: Intersection value of the initialtion point
 
-For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter. __Note: They are for normalized data.__
+For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter
 
+They are the parameters associated with parameter "maximum"
+
+* `maximum_N_Estimate`: Here N stand for the intersection in the normalized scale
+* `maximum_Std_Error`
+* `maximum_t_value`
+* `maximum_Pr_t`
 
 They are the parameters associated with parameter "slope"
 
-* `slope_N_Estimate`: here N stand for the slope in the normalized scale
+* `slope_N_Estimate`: Here N stand for the intersection in the normalized scale
 * `slope_Std_Error`
-* `slope_t_value`    
+* `slope_t_value`
 * `slope_Pr_t`
 
-They are the parameters associated with parameter "intersection"
-
-* `intersection_N_Estimate`  here N stand for the intersection in the normalized scale
-* `intersection_Std_Error`  
-* `intersection_t_value`  
-* `intersection_Pr_t` 
-
+They are the parameters associated with parameter "midpoint"
 
-Here are the fit-parameters that are not related with individual variable that is fitted, but gives information about overal fit.
+* `midPoint_N_Estimate`: Here N stand for the intersection in the normalized scale
+* `midPoint_Std_Error`
+* `midPoint_t_value`
+* `midPoint_Pr_t`
 
 They are the parameters associated with the quality of the fit. 
 
@@ -176,26 +180,25 @@ They are the parameters associated with the quality of the fit.
 * `AIC_value`: Smaller value indicate a better fit
 * `BIC_value`: Smaller value indicate a better fit
 
-Final results that are relavent to most of the users   
-
-They are the fitter values after converting everything from normalized to un-normalized scale.
-
-* `intersection_Estimate`: Intersection estimate for the raw data
-* `slope_Estimate`: Slope estimate for the raw data
+They are the fitted values after converting everything from normalized to un-normalized scale. 
 
+* `maximum_Estimate`Maximum intensity estimate for the raw data
+* `slope_Estimate`: __Slope parameter__ estimate for the raw data 
+* `midPoint_Estimate`: Mid-point estimate (time the intensity reaches 1/2 of maximum) for the raw data
 
 ## Check the results to see if the results are meaningfull
 
-By using the `intersection_Estimate`, `slope_Estimate` parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::lineFitFormula()`. We can draw the bes line on top of our initial data.
+By using the `maximum_Estimate`, `slope_Estimate`, `midPoint_Estimate` parameters of the sigmoidalfit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::sigmoidalFitFormula()`. We can draw the best sigmoidal fit on top of our initial data.
 
 ```{r plot raw data and fit, fig.height=4, fig.width=8}
-# intensityTheoretical=lineFitFormula(time,
-#                                     slope=parameterVector$slope_Estimate,
-#                                     intersection=parameterVector$intersection_Estimate)
-# comparisonData=cbind(dataInput,intensityTheoretical)
-# 
-# ggplot(comparisonData)+
-#   geom_point(aes(x=time, y=intensity))+
-#   geom_line(aes(x=time,y=intensityTheoretical))+
-#   expand_limits(x = 0, y = 0)
-```
\ No newline at end of file
+intensityTheoretical=sicegar::sigmoidalFitFormula(time,
+                                                  maximum=parameterVector$maximum_Estimate,
+                                                  slope=parameterVector$slope_Estimate,
+                                                  midPoint=parameterVector$midPoint_Estimate)
+comparisonData=cbind(dataInput,intensityTheoretical)
+
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
+```
diff --git a/inst/doc/sigmoidal_vignette.html b/inst/doc/sigmoidal_vignette.html
index 95209da..d9ecd82 100644
--- a/inst/doc/sigmoidal_vignette.html
+++ b/inst/doc/sigmoidal_vignette.html
@@ -12,7 +12,7 @@
 
 
 
-
+
 
 (2/3) Sigmoidal model
 
@@ -70,13 +70,11 @@
 
 
    (2/3) Sigmoidal model
    
 
    Umut Caglar
    
-
    2017-03-10
    
+
    2017-03-15
    
 
 
 
-
    
-
    The Sigmoidal Fit Function
    
-
    This is a document invetigates details of sigmoidal model
    
+
    # The Sigmoidal Fit Function This is a document invetigates details of sigmoidal model
    
 
    
 
    Data generation
    
 
    To simulate the results, we will go backwards and firstly generate some data to analize. To add some randomness to the input data I will use some noise. The input of all package must be in the form of a data frame with at least 2 columns time and intensity.
    
@@ -114,8 +112,8 @@ 
    Data normalization
    
 intensityData=dataInput$intensity-intensityMin
 intensityData=intensityData/intensityRatio

    The normalization code is

    -
    normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
    +
    normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")

    Components of the normalization output

    head(normalizedInput$timeIntensityData) # the normalized time and intensity data
    ##        time   intensity
@@ -133,12 +131,12 @@ 
    Data normalization
    
 
 
    
 
    The figures of raw and normalized datasets
    
-
    
+
    
 
    
 
    
 
    Sigmoidal fit of the data
    
 
    Now it is time to calculate the parameters by using sicegar::sigmoidalFitFunction()
    
-
    parameterVector<-sigmoidalFitFunction(normalizedInput,tryCounter=2)
+
    parameterVector<-sicegar::sigmoidalFitFunction(normalizedInput,tryCounter=2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
 
@@ -175,8 +173,8 @@ 
    Sigmoidal fit of the data
    
 ## maximum_Estimate                     "4.054168"    
 ## slope_Estimate                       "1.038046"    
 ## midPoint_Estimate                    "8.046254"
    
-
    Here is the brief explanations of the parameters that are given by lineFitFunction (In different order then than the output vector of the lineFitFunction)
    
-
    These are the parameters of the normalization step:
    
+
    Here is the brief explanations of the parameters that are given by sicegar::sigmoidalFitFunction (In different order then than the output vector of the sigmoidalFitFunction)
    
+
    These are the parameters of the normalization step
    
 
      
 
    • dataScalingParameters.timeRatio: Maximum of raw time data
      
 
      • 
@@ -189,30 +187,34 @@ 
      Sigmoidal fit of the data
      
 
    • model: Gives the used model for fitting
      • 
 
    • isThisaFit: FALSE means there is not any successful fit. TRUE means there is at least one successful fit
      • 
 
    
-
    Likelihood maximization algorithm starts from a random initiation point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm.
    
+
    Likelihood maximization algorithm starts from a random initiation (if tryCounter!=1) point and goes down the fitness space by a gradient decent algorithm. these parameters represent the start point of the gradient decent algorithm.
    
 
      
-
    • startVector.slope: Slope value of the initialtion point
      • 
-
    • startVector.intersection: Intersection value of the initialtion point
      • 
+
    • startVector.maximum: Maximum value of the initiation point
      • 
+
    • startVector.slope: Slope value of the initiation point
      • 
+
    • startVector.midPoint: Mid-point value of the initiation point
      • 
+
    
+
    For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter
    
+
    They are the parameters associated with parameter “maximum”
    
+
      
+
    • maximum_N_Estimate: Here N stand for the intersection in the normalized scale
      • 
+
    • maximum_Std_Error
      • 
+
    • maximum_t_value
      • 
+
    • maximum_Pr_t
      • 
 
    
-
    For each parameter that needs to fitted by LM algorithm; the algorithm gives a bunch of statistical parameters; including the estimated value of the parameter. Note: They are for normalized data.
    
 
    They are the parameters associated with parameter “slope”
    
 
      
-
    • slope_N_Estimate: here N stand for the slope in the normalized scale
      • 
+
    • slope_N_Estimate: Here N stand for the intersection in the normalized scale
      • 
 
    • slope_Std_Error
      • 
-
    • slope_t_value
      
-
      • 
+
    • slope_t_value
      • 
 
    • slope_Pr_t
      • 
 
    
-
    They are the parameters associated with parameter “intersection”
    
+
    They are the parameters associated with parameter “midpoint”
    
 
      
-
    • intersection_N_Estimate here N stand for the intersection in the normalized scale
      • 
-
    • intersection_Std_Error
      
-
      • 
-
    • intersection_t_value
      
-
      • 
-
    • intersection_Pr_t
      • 
+
    • midPoint_N_Estimate: Here N stand for the intersection in the normalized scale
      • 
+
    • midPoint_Std_Error
      • 
+
    • midPoint_t_value
      • 
+
    • midPoint_Pr_t
      • 
 
    
-
    Here are the fit-parameters that are not related with individual variable that is fitted, but gives information about overal fit.
    
 
    They are the parameters associated with the quality of the fit.
    
 
      
 
    • residual_Sum_of_Squares: Small value indicate better fit
      • 
@@ -220,26 +222,27 @@ 
      Sigmoidal fit of the data
      
 
    • AIC_value: Smaller value indicate a better fit
      • 
 
    • BIC_value: Smaller value indicate a better fit
      • 
 
    
-
    Final results that are relavent to most of the users
    
-
    They are the fitter values after converting everything from normalized to un-normalized scale.
    
+
    They are the fitted values after converting everything from normalized to un-normalized scale.
    
 
      
-
    • intersection_Estimate: Intersection estimate for the raw data
      • 
-
    • slope_Estimate: Slope estimate for the raw data
      • 
+
    • maximum_EstimateMaximum intensity estimate for the raw data
      • 
+
    • slope_Estimate: Slope parameter estimate for the raw data
      • 
+
    • midPoint_Estimate: Mid-point estimate (time the intensity reaches 1/2 of maximum) for the raw data
      • 
 
    
 
    
 
    
 
    Check the results to see if the results are meaningfull
    
-
    By using the intersection_Estimate, slope_Estimate parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of sicegar::lineFitFormula(). We can draw the bes line on top of our initial data.
    
-
    # intensityTheoretical=lineFitFormula(time,
-#                                     slope=parameterVector$slope_Estimate,
-#                                     intersection=parameterVector$intersection_Estimate)
-# comparisonData=cbind(dataInput,intensityTheoretical)
-# 
-# ggplot(comparisonData)+
-#   geom_point(aes(x=time, y=intensity))+
-#   geom_line(aes(x=time,y=intensityTheoretical))+
-#   expand_limits(x = 0, y = 0)
    
-
    
+
    By using the maximum_Estimate, slope_Estimate, midPoint_Estimate parameters of the sigmoidalfit and the time sequence that we already created we can calculate the intensity values by the help of sicegar::sigmoidalFitFormula(). We can draw the best sigmoidal fit on top of our initial data.
    
+
    intensityTheoretical=sicegar::sigmoidalFitFormula(time,
+                                                  maximum=parameterVector$maximum_Estimate,
+                                                  slope=parameterVector$slope_Estimate,
+                                                  midPoint=parameterVector$midPoint_Estimate)
+comparisonData=cbind(dataInput,intensityTheoretical)
+
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
    
+
    
 
    
 
 
diff --git a/man/categorize.Rd b/man/categorize.Rd
index b48c694..20c783f 100644
--- a/man/categorize.Rd
+++ b/man/categorize.Rd
@@ -7,7 +7,8 @@
 categorize(parameterVectorLinear, parameterVectorSigmoidal,
   parameterVectorDoubleSigmoidal, threshold_line_slope_parameter = 0.01,
   threshold_intensity_interval = 0.1,
-  threshold_minimum_for_intensity_maximum = 0, threshold_difference_AIC = 0,
+  threshold_minimum_for_intensity_maximum = 0.3,
+  threshold_difference_AIC = 0,
   threshold_lysis_finalAsymptoteIntensity = 0.75, threshold_AIC = -10)
 }
 \arguments{
diff --git a/man/categorize_nosignal.Rd b/man/categorize_nosignal.Rd
index 679f762..b6f91ad 100644
--- a/man/categorize_nosignal.Rd
+++ b/man/categorize_nosignal.Rd
@@ -6,7 +6,7 @@
 \usage{
 categorize_nosignal(parameterVectorLinear,
   threshold_line_slope_parameter = 0.01, threshold_intensity_interval = 0.1,
-  threshold_minimum_for_intensity_maximum = 0)
+  threshold_minimum_for_intensity_maximum = 0.3)
 }
 \arguments{
 \item{parameterVectorLinear}{is the output of lineFitFunction.}
diff --git a/mathematica/candidate functions with explanations.m b/mathematica/candidate functions with explanations.m
deleted file mode 100644
index 53dbcbe..0000000
--- a/mathematica/candidate functions with explanations.m	
+++ /dev/null
@@ -1,394 +0,0 @@
-(* ::Package:: *)
-
-(************************************************************************)
-(* This file was generated automatically by the Mathematica front end.  *)
-(* It contains Initialization cells from a Notebook file, which         *)
-(* typically will have the same name as this file except ending in      *)
-(* ".nb" instead of ".m".                                               *)
-(*                                                                      *)
-(* This file is intended to be loaded into the Mathematica kernel using *)
-(* the package loading commands Get or Needs.  Doing so is equivalent   *)
-(* to using the Evaluate Initialization Cells menu command in the front *)
-(* end.                                                                 *)
-(*                                                                      *)
-(* DO NOT EDIT THIS FILE.  This entire file is regenerated              *)
-(* automatically each time the parent Notebook file is saved in the     *)
-(* Mathematica front end.  Any changes you make to this file will be    *)
-(* overwritten.                                                         *)
-(************************************************************************)
-
-
-
-(* ::Code::RGBColor[1, 0, 0]:: *)
-Clear["Global`*"]
-
-
-fSigmoidal[A_,Ka_,B_,M_,x_]=A+(Ka-A)/(1+E^(-B*(x-M)));
-Manipulate[
-Plot[fSigmoidal[A,Ka,B,M,x],{x,0,15},PlotRange->{0,1}],
-{{A,0},0,1,.01},
-{{Ka,1},0,1,.01},
-{{B,1},0,10,.01},
-{{M,8},0,15,.01}]
-
-
-Limit[fSigmoidal[A,Ka,B,M,x],x->-\[Infinity],Assumptions->{B>0}]
-Limit[fSigmoidal[A,Ka,B,M,x],x->\[Infinity],Assumptions->{B>0}]
-Solve[D[fSigmoidal[A,Ka,B,M,x],{x,2}]==0,x]
-D[fSigmoidal[A,Ka,B,M,x],x]/.x->M (* As can be seen from the result the slope is also 
-related with (Ka-A); i.e the width of the function. But if the data is normalized in 
-such a way Ka=1 and A=0 slope is directly proportional to B*)
-
-
-fNegativeSigmoidal[A_,Ka_,B_,M_,x_]=A+(Ka-A)/(1+E^(B*(x-M)));
-Manipulate[
-Plot[fNegativeSigmoidal[A,Ka,B,M,x],{x,0,15},PlotRange->{0,1}],
-{{A,0},0,1,.01},
-{{Ka,1},0,1,.01},
-{{B,1},0,10,.01},
-{{M,8},0,15,.01}]
-
-
-fMultiplicationSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-											(A1+(Ka-A1)/(1+E^(-B1*(x-M1))))*(A2+(1-A2)/(1+E^(B2*(x-(M1+L)))));
-Manipulate[
-Plot[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},PlotRange->{0,1.2}],
-{{A1,0},0,1,.01},
-{{A2,0.2},0,1,.01},
-{{Ka,1},0,1,.01},
-{{B1,1},0,10,.01},
-{{M1,8},0,20,.01},
-{{B2,2},0,10,0.01},
-{{L,10},0,10,0.001}]
-
-
-Limit[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->-\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-Limit[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-
-
-Solve[D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0,x]
-Reduce[{D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-Solve[D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0,x]
-Reduce[{D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-
-Plot[fMultiplicationSigmoidal[0,0.5368628,1.454867,1.084971,11.11337,8.529749,1.13329,x]
-,{x,0,30},PlotRange->{0,1.2}]
-
-
-fDMultiplicationSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-	D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}];
-
-Plot[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x]/.
-	{A1->0,A2->0.5368628,Ka->1.454867,B1->1.084971,M1->11.11337,B2->8.529749,L->1.13329},
-																{x,0,30},PlotRange->Full]
-
-
-xValue=2000;
-
-Manipulate[
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-							Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,-xValue]]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-							Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,xValue]]]},
-		{Plot[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-							PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-							PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All],
-
-	{{A1,0},0,1,.01},
-	{{A2,0.5368628},0,1,.01},
-	{{Ka,1.454867},0,2,.01},
-	{{B1,1.084971},0.01,10,.01},
-	{{M1,11.11337},7.5-20,7.5+20,.01},
-	{{B2,8.529749},0.01,10,0.01},
-	{{L,1.13329},0,10,0.001}
-]
-
-
-xValue=2000;
-Subscript[A1, 0]=0; Subscript[A2, 0]=0.06; Subscript[Ka, 0]=2; Subscript[B1, 0]=1.08497; Subscript[M1, 0]=11.1134; Subscript[B2, 0]=2.14; Subscript[L, 0]=1.13329;
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-			Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,-xValue]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]}]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-			Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,xValue]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]}]]},
-		{Plot[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]},{x,0,30},
-					PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]},{x,0,30},
-					PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All]
-
-
-xValue=2000;
-
-fProtoAdditiveSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))));
-fDProtoAdditiveSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=
-									D[fProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,x],{x,1}];
-
-Manipulate[
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-								Sign[fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,-xValue]]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-								Sign[fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,xValue]]]},
-		{Plot[fProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,x],{x,0,30},
-								PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,x],{x,0,30},
-								PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All],
-
-	{{Ka,1},0,2,.01},
-	{{B1,1},0.01,10,.01},
-	{{M1,6},7.5-20,7.5+20,.01},
-	{{B2,2},0.01,10,0.01},
-	{{L,10},0,10,0.001}
-]
-
-
-fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,M1]
-
-
-Subscript[B1, 0]=1; Subscript[M1, 0]=6; Subscript[B2, 0]=2; Subscript[L, 0]=10;
-
-fProtoAdditiveSigmoidal2[B1_,M1_,B2_,L_,x_]:=1/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))));
-fProtoAdditiveSigmoidalN[B1_,M1_,B2_,L_,x_]:=
-	fProtoAdditiveSigmoidal2[B1,M1,B2,L,x]/Round[NMaximize[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x0],x0][[1]],0.000001]
-
-fProtoAdditiveSigmoidalN[1,6,2,10,x]
-				
-
-(*Plot[fProtoAdditiveSigmoidalN[1,6,2,10,x],{x,0,30}]*)
-
-
-D[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x],x]==0
-
-
-NMaximize[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x0],x0]/.{B1->.1,M1->6,B2->2,L->10}
-u=Solve[Normal[Series[(B2-B1)*E^(B2*x-B1*x-B2*L)-B1*E^(-B1*x)+B2*E^(B2*x-B2*L),{x,L/2,13}]]==0,x][[1]][[1]][[2]];
-N[u+M1/.{B1->1,M1->6,B2->2,L->5}]
-
-
-Subscript[y, line1]=(x-x0)*Subscript[m, line1]+y0/.{x0->0, Subscript[m, line1]->B1/4, y0->1/2};
-Subscript[y, line2]=(x-x0)*Subscript[m, line2]+y0/.{x0->L, Subscript[m, line2]->-B2/4, y0->1/2};
-
-slope1=Coefficient[Subscript[y, line1],x,1]
-intersection1=Coefficient[Subscript[y, line1],x,0]
-slope2=Coefficient[Subscript[y, line2],x,1]
-intersection2=Coefficient[Subscript[y, line2],x,0]
-
-
-xIntersection=Simplify[(intersection2-intersection1)/(slope1-slope2)]
-
-
-NMaximize[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x0],x0]/.{B1->0.02,M1->6,B2->2,L->2}
-u=Solve[Normal[Series[(B2-B1)*E^(B2*x-B1*x-B2*L)-B1*E^(-B1*x)+B2*E^(B2*x-B2*L),{x,((B1+3 B2) L)/(4 (B1+B2)),3}]]==0,x][[1]][[1]][[2]];
-N[u+M1/.{B1->0.02,M1->6,B2->2,L->2}]
-{L/2+M1,(((B1+3 B2) L)/(4 (B1+B2))+M1)}/.{B1->0.02,M1->6,B2->2,L->2}
-
-
-Simplify[fSigmoidal[A,Ka,B,M,x]+fSigmoidal[A,Ka,-B,M,x]]
-
-
-Manipulate[
-	Grid[
-		{
-		{Plot[fSigmoidal[A,Ka,B,M,x],{x,0,15},PlotRange->{-0.2,1.2},
-				PlotLabel->Function,ImageSize->200]},
-		{Plot[fSigmoidal[A,Ka,-B,M,x],{x,0,15},PlotRange->{-0.2,1.2},
-				PlotLabel->Function,ImageSize->200]},
-		{Plot[fSigmoidal[A,Ka,B,M,x]+fSigmoidal[A,Ka,-B,M,x],{x,0,15},PlotRange->{-0.2,1.2},
-				PlotLabel->Function,ImageSize->200]}
-		}
-	],
-	{{A,0},0,1,.01},
-	{{Ka,1},0,1,.01},
-	{{B,1},0,10,.01},
-	{{M,8},0,15,.01}
-]
-
-
-fLeftAdditionSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))))+Ka/(1+E^(B1*(x-M1))); 
-(* Not the sign change in B1 terms*)
-fRightSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/(1+E^(B2*(x-(M1+L))));
-
-Manipulate[
-	Grid[
-		{
-		{Plot[{fLeftAdditionSigmoidal[Ka,B1,M1,B2,L,x],fRightSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Added Function",ImageSize->300]},
-		{Plot[{fLeftAdditionSigmoidal[Ka,B1,M1,B2,L,x]-fRightSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Difference Function",ImageSize->300]}
-		}
-	],
-
-	{{Ka,1},0,1,.01},
-	{{B1,1},0.01,10,.001},
-	{{M1,15},7.5-20,7.5+20,.01},
-	{{B2,2},0.01,10,0.001},
-	{{L,1},0,10,0.001}
-]
-
-
-fRightAdditionSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))))+Ka/(1+E^(-B2*(x-(M1+L)))); 
-(* Not the sign change in B2 terms*)
-fLeftSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/(1+E^(-B1*(x-M1)));
-
-Manipulate[
-	Grid[
-		{
-		{Plot[{fRightAdditionSigmoidal[Ka,B1,M1,B2,L,x],fLeftSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Added Function",ImageSize->300]},
-		{Plot[{fRightAdditionSigmoidal[Ka,B1,M1,B2,L,x]-fLeftSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Difference Function",ImageSize->300]}
-		}
-	],
-
-	{{Ka,1},0,1,.01},
-	{{B1,1},0.01,10,.001},
-	{{M1,15},7.5-20,7.5+20,.01},
-	{{B2,2},0.01,10,0.001},
-	{{L,10},0,10,0.001}
-]
-
-
-fAdditionalSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-			(Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L))))))+((Ka*A2)/(1+E^(-B2*(x-(M1+L)))))+((Ka*A1)/(1+E^(B1*(x-M1))));
-
-Manipulate[
-	Plot[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},PlotRange->{0,1.2}],
-	{{A1,0},0,1,.01},
-	{{A2,0.2},0,1,.01},
-	{{Ka,1},0,1,.01},
-	{{B1,1},0,10,.01},
-	{{M1,8},0,20,.01},
-	{{B2,2},0,10,0.01},
-	{{L,10},0,10,0.001}
-]
-
-
-Limit[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->-\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-Limit[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-
-
-Solve[D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0 ,x]
-Reduce[{D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-Solve[D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0,x]
-Reduce[{D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-
-fDAdditionalSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-						D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}];
-
-
-xValue=2000;
-
-Manipulate[
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-									Sign[fDAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,-xValue]]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-									Sign[fDAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,xValue]]]},
-		{Plot[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-									PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-									PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All],
-
-	{{A1,0},0,1,.01},
-	{{A2,0.5368628},0,1,.01},
-	{{Ka,1.454867},0,2,.01},
-	{{B1,1.084971},0.01,10,.01},
-	{{M1,11.11337},7.5-20,7.5+20,.01},
-	{{B2,8.529749},0.01,10,0.01},
-	{{L,1.13329},0,10,0.001}
-]
-
-
-Plot[fAdditionalSigmoidal[0,1,1,8.88,19.5,0.121,0,x],{x,0,30},PlotRange->{0,1.2},ImageSize->350]
-
-
-A11=0; A22=0.7; Kaa=1; B11=4; M11=15; B22=2; LL=1; epsilon=0.02;
-(*Plot[1/((1+\[ExponentialE]^(-B11*(x-M11)))*(1+\[ExponentialE]^(B22*(x-(M11+LL))))),{x,0,30},PlotRange\[Rule]Full,ImageSize\[Rule]350]
-
-Plot[Log[1/((1+\[ExponentialE]^(-B11*(x-M11)))*(1+\[ExponentialE]^(B22*(x-(M11+LL)))))],{x,0,30},PlotRange\[Rule]Full,ImageSize\[Rule]350]*)
-
-argument=FindArgMax[Log[(1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL))))))],{x,M11+LL/2}][[1]]
-const=1/((1+E^(-B11*(argument-M11)))*(1+E^(B22*(argument-(M11+LL)))))
-
-
-
-
-
-rightSide[x_]:=UnitStep[x-argument-epsilon]*(1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL)))))*(Kaa-A22*Kaa)/const+A22*Kaa);
-leftSide[x_]:=(1-UnitStep[x-argument-epsilon])*(1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL)))))*(Kaa-A11*Kaa)/const+A11*Kaa);
-wholeFunction[x_]:=rightSide[x]+leftSide[x]
-
-
-
-
-
-
-derivativeRightSide[x_]=D[(1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL)))))*(Kaa-A22*Kaa)/const+A22*Kaa),{x,1}]*UnitStep[x-argument-epsilon];
-derivativeLeftSide[x_]=D[(1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL)))))*(Kaa-A11*Kaa)/const+A11*Kaa),{x,1}]*(1-UnitStep[x-argument-epsilon]);
-wholeDerivativeFunction[x_]:=derivativeRightSide[x]+derivativeLeftSide[x]
-
-
-M11Numeric=FindArgMax[derivativeLeftSide[x],{x,M11}][[1]];
-B11Numeric=wholeDerivativeFunction[M11Numeric];
-M22Numeric=FindArgMax[Abs[derivativeRightSide[x]],{x,M11+LL}][[1]];
-B22Numeric=wholeDerivativeFunction[M22Numeric];
-M11mid=x/.FindRoot[wholeFunction[x]==((Kaa-A11)/2+A11),{x,M11}][[1]]
-M22mid=x/.FindRoot[wholeFunction[x]==((Kaa-A22)/2+A22),{x,M11+LL}][[1]]
-A11Numeric=wholeFunction[0];
-A22Numeric=wholeFunction[30];
-
-Grid[{
-	{Grid[{
-		{Plot[rightSide[x],{x,0,30},PlotRange->{0,1.2},ImageSize->250],
-		Plot[leftSide[x],{x,0,30},PlotRange->{0,1.2},ImageSize->250]}
-	},Frame->All]},
-	{Plot[
-		{wholeFunction[x],Kaa,fSigmoidal[A11,Kaa,B11,M11mid,x]}
-			,{x,14,16},PlotRange->{0,1.2},ImageSize->350]},
-	{Plot[{wholeDerivativeFunction[x]},{x,0,30},PlotRange->Full,ImageSize->350]}
-},Frame->All]
-
-m={{,"original","simulation"},
-{"A11",A11,A11Numeric},
-{"A22",A22,A22Numeric},
-{"Kaa",Kaa,Kaa},
-{"B11",(1/4)*(Kaa-A11)*B11,B11Numeric},
-{"M11",M11,M11Numeric},
-{"M11_mid",M11,M11mid},
-{"B22",-(1/4)*(Kaa-A22)*B22,B22Numeric},
-{"LL",LL,M22Numeric-M11Numeric},
-{"LL_mid",LL,M22mid-M11mid}};
-Grid[m,Frame->All]
-
-
-
diff --git a/mathematica/candidate functions with explanations.nb b/mathematica/candidate functions with explanations.nb
deleted file mode 100644
index ac256d4..0000000
--- a/mathematica/candidate functions with explanations.nb	
+++ /dev/null
@@ -1,10401 +0,0 @@
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-
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-(* http://www.wolfram.com/nb *)
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-(* Beginning of Notebook Content *)
-Notebook[{
-
-Cell[CellGroupData[{
-Cell["ANALYSIS OF SINGLE CELL VIRUS INFECTIONS", "Subtitle",
- CellChangeTimes->{
-  3.621784640624446*^9, {3.6217846747934*^9, 3.6217846903612905`*^9}}],
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-Cell[CellGroupData[{
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-Cell["Introduction", "Subsection",
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-Cell["before using this notebook", "Text",
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-Cell["", "Text"],
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-Cell["\<\
-The main idea is to model behaviour of infections in a single cell with a \
-functional model.
-
-The data can be classified into 3 main groups by just bare eye investigation\
-\>", "Text",
- CellChangeTimes->{{3.621785057055264*^9, 3.621785059746418*^9}, {
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-
-Cell["The ones with no infection", "Item1",
- CellChangeTimes->{{3.621785458315215*^9, 3.621785468478796*^9}}],
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-Cell["\<\
-the ones with infection, but stay alive at the end of the experiment\
-\>", "Item1",
- CellChangeTimes->{{3.621785469857875*^9, 3.6217854888169594`*^9}}],
-
-Cell["Ones that die because of infection before experiment ends", "Item1",
- CellChangeTimes->{{3.621785490088032*^9, 3.621785522588891*^9}}]
-}, Open  ]],
-
-Cell["", "Text",
- CellChangeTimes->{3.6217858230880785`*^9}],
-
-Cell["One candinate model might be ", "Text",
- CellChangeTimes->{{3.6217855361816683`*^9, 3.6217856042665625`*^9}}],
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-Cell[CellGroupData[{
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-Cell["A single line to represent data with no infection", "Item1",
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-progress and end of the infection (i.e death of the cell)\
-\>", "Item1",
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-}, Open  ]],
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-Cell["", "Text",
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-Te aim of this text is to investigate possible candidates for a double \
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-It is kind of obvious to see the problem with bare eye, but it is hard to \
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-This can be seen more clearly if we look at the plot of the derivative of the \
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-  PlotRangeClipping->True,
-  PlotRangePadding->{
-    Scaled[0.02], Automatic}]], "Output",
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-    0.9400000000000001, $CellContext`L$$ = 5.035, $CellContext`M1$$ = 
-    9.580000000000002, Typeset`show$$ = True, Typeset`bookmarkList$$ = {}, 
-    Typeset`bookmarkMode$$ = "Menu", Typeset`animator$$, Typeset`animvar$$ = 
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-    Typeset`skipInitDone$$ = True, $CellContext`A1$802$$ = 
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-    DynamicBox[Manipulate`ManipulateBoxes[
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-      "Variables" :> {$CellContext`A1$$ = 0, $CellContext`A2$$ = 
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-      "ControllerVariables" :> {
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-        Hold[$CellContext`A2$$, $CellContext`A2$803$$, 0], 
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-      "Specifications" :> {{{$CellContext`A1$$, 0}, 0, 1, 
-         0.01}, {{$CellContext`A2$$, 0}, 0, 1, 0.01}, {{$CellContext`Ka$$, 1},
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-         0.01}, {{$CellContext`B2$$, 2}, 0.01, 10, 
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-  3.6217783518613405`*^9, 3.62178044850767*^9, 3.62178179403263*^9}]
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diff --git a/mathematica/presentation.m b/mathematica/presentation.m
deleted file mode 100644
index 144f5f9..0000000
--- a/mathematica/presentation.m
+++ /dev/null
@@ -1,350 +0,0 @@
-(* ::Package:: *)
-
-(************************************************************************)
-(* This file was generated automatically by the Mathematica front end.  *)
-(* It contains Initialization cells from a Notebook file, which         *)
-(* typically will have the same name as this file except ending in      *)
-(* ".nb" instead of ".m".                                               *)
-(*                                                                      *)
-(* This file is intended to be loaded into the Mathematica kernel using *)
-(* the package loading commands Get or Needs.  Doing so is equivalent   *)
-(* to using the Evaluate Initialization Cells menu command in the front *)
-(* end.                                                                 *)
-(*                                                                      *)
-(* DO NOT EDIT THIS FILE.  This entire file is regenerated              *)
-(* automatically each time the parent Notebook file is saved in the     *)
-(* Mathematica front end.  Any changes you make to this file will be    *)
-(* overwritten.                                                         *)
-(************************************************************************)
-
-
-
-(* ::Code::RGBColor[1, 0, 0]:: *)
-Clear["Global`*"]
-
-
-fSigmoidal[A_,Ka_,B_,M_,x_]=A+(Ka-A)/(1+E^(-B*(x-M)));
-Manipulate[
-Plot[fSigmoidal[A,Ka,B,M,x],{x,0,15},PlotRange->{0,1}],
-{{A,0},0,1,.01},
-{{Ka,1},0,1,.01},
-{{B,1},0,10,.01},
-{{M,8},0,15,.01}]
-
-
-Limit[fSigmoidal[A,Ka,B,M,x],x->-\[Infinity],Assumptions->{B>0}]
-Limit[fSigmoidal[A,Ka,B,M,x],x->\[Infinity],Assumptions->{B>0}]
-Solve[D[fSigmoidal[A,Ka,B,M,x],{x,2}]==0,x,Reals]
-D[fSigmoidal[A,Ka,B,M,x],x]/.x->M (* As can be seen from the result the slope is also 
-related with (Ka-A); i.e the width of the function. But if the data is normalized in 
-such a way Ka=1 and A=0 slope is directly proportional to B*)
-
-
-fNegativeSigmoidal[A_,Ka_,B_,M_,x_]=A+(Ka-A)/(1+E^(B*(x-M)));
-Manipulate[
-Plot[fNegativeSigmoidal[A,Ka,B,M,x],{x,0,15},PlotRange->{0,1}],
-{{A,0},0,1,.01},
-{{Ka,1},0,1,.01},
-{{B,1},0,10,.01},
-{{M,8},0,15,.01}]
-
-
-fMultiplicationSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-											(A1+(Ka-A1)/(1+E^(-B1*(x-M1))))*(A2+(1-A2)/(1+E^(B2*(x-(M1+L)))));
-Manipulate[
-Plot[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},PlotRange->{0,1.2}],
-{{A1,0},0,1,.01},
-{{A2,0.2},0,1,.01},
-{{Ka,1},0,1,.01},
-{{B1,1},0,10,.01},
-{{M1,8},0,20,.01},
-{{B2,2},0,10,0.01},
-{{L,10},0,10,0.001}]
-
-
-Limit[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->-\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-Limit[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-
-
-Solve[D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0,x]
-Reduce[{D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-Solve[D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0,x]
-Reduce[{D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-
-Plot[fMultiplicationSigmoidal[0,0.5368628,1.454867,1.084971,11.11337,8.529749,1.13329,x]
-,{x,0,30},PlotRange->{0,1.2}]
-
-
-fDMultiplicationSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-	D[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}];
-
-Plot[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x]/.
-	{A1->0,A2->0.5368628,Ka->1.454867,B1->1.084971,M1->11.11337,B2->8.529749,L->1.13329},
-																{x,0,30},PlotRange->Full]
-
-
-xValue=2000;
-
-Manipulate[
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-							Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,-xValue]]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-							Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,xValue]]]},
-		{Plot[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-							PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-							PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All],
-
-	{{A1,0},0,1,.01},
-	{{A2,0.5368628},0,1,.01},
-	{{Ka,1.454867},0,2,.01},
-	{{B1,1.084971},0.01,10,.01},
-	{{M1,11.11337},7.5-20,7.5+20,.01},
-	{{B2,8.529749},0.01,10,0.01},
-	{{L,1.13329},0,10,0.001}
-]
-
-
-xValue=2000;
-Subscript[A1, 0]=0; Subscript[A2, 0]=0.06; Subscript[Ka, 0]=2; Subscript[B1, 0]=1.08497; Subscript[M1, 0]=11.1134; Subscript[B2, 0]=2.14; Subscript[L, 0]=1.13329;
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-			Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,-xValue]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]}]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-			Sign[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,xValue]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]}]]},
-		{Plot[fMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]},{x,0,30},
-					PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDMultiplicationSigmoidal[A1,A2,Ka,B1,M1,B2,L,x]/.
-				{A1->Subscript[A1, 0], A2->Subscript[A2, 0], Ka->Subscript[Ka, 0], B1->Subscript[B1, 0], M1->Subscript[M1, 0], B2->Subscript[B2, 0],L->Subscript[L, 0]},{x,0,30},
-					PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All]
-
-
-xValue=2000;
-
-fProtoAdditiveSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))));
-fDProtoAdditiveSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=
-									D[fProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,x],{x,1}];
-
-Manipulate[
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-								Sign[fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,-xValue]]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-								Sign[fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,xValue]]]},
-		{Plot[fProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,x],{x,0,30},
-								PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDProtoAdditiveSigmoidal[Ka,B1,M1,B2,L,x],{x,0,30},
-								PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All],
-
-	{{Ka,1},0,2,.01},
-	{{B1,1},0.01,10,.01},
-	{{M1,6},7.5-20,7.5+20,.01},
-	{{B2,2},0.01,10,0.01},
-	{{L,10},0,10,0.001}
-]
-
-
-Subscript[B1, 0]=1; Subscript[M1, 0]=6; Subscript[B2, 0]=2; Subscript[L, 0]=10;
-
-fProtoAdditiveSigmoidal2[B1_,M1_,B2_,L_,x_]:=1/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))));
-fProtoAdditiveSigmoidalN[B1_,M1_,B2_,L_,x_]:=
-	fProtoAdditiveSigmoidal2[B1,M1,B2,L,x]/Round[NMaximize[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x0],x0][[1]],0.000001]
-
-fProtoAdditiveSigmoidalN[1,6,2,10,x]
-				
-
-(*Plot[fProtoAdditiveSigmoidalN[1,6,2,10,x],{x,0,30}]*)
-
-
-D[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x],x]==0
-
-
-NMaximize[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x0],x0]/.{B1->.1,M1->6,B2->2,L->10}
-u=Solve[Normal[Series[(B2-B1)*E^(B2*x-B1*x-B2*L)-B1*E^(-B1*x)+B2*E^(B2*x-B2*L),{x,L/2,13}]]==0,x][[1]][[1]][[2]];
-N[u+M1/.{B1->1,M1->6,B2->2,L->5}]
-
-
-Subscript[y, line1]=(x-x0)*Subscript[m, line1]+y0/.{x0->0, Subscript[m, line1]->B1/4, y0->1/2};
-Subscript[y, line2]=(x-x0)*Subscript[m, line2]+y0/.{x0->L, Subscript[m, line2]->-B2/4, y0->1/2};
-
-slope1=Coefficient[Subscript[y, line1],x,1]
-intersection1=Coefficient[Subscript[y, line1],x,0]
-slope2=Coefficient[Subscript[y, line2],x,1]
-intersection2=Coefficient[Subscript[y, line2],x,0]
-
-
-xIntersection=Simplify[(intersection2-intersection1)/(slope1-slope2)]
-
-
-NMaximize[fProtoAdditiveSigmoidal2[B1,M1,B2,L,x0],x0]/.{B1->0.02,M1->6,B2->2,L->2}
-u=Solve[Normal[Series[(B2-B1)*E^(B2*x-B1*x-B2*L)-B1*E^(-B1*x)+B2*E^(B2*x-B2*L),{x,((B1+3 B2) L)/(4 (B1+B2)),3}]]==0,x][[1]][[1]][[2]];
-N[u+M1/.{B1->0.02,M1->6,B2->2,L->2}]
-{L/2+M1,(((B1+3 B2) L)/(4 (B1+B2))+M1)}/.{B1->0.02,M1->6,B2->2,L->2}
-
-
-Simplify[fSigmoidal[A,Ka,B,M,x]+fSigmoidal[A,Ka,-B,M,x]]
-
-
-Manipulate[
-	Grid[
-		{
-		{Plot[fSigmoidal[A,Ka,B,M,x],{x,0,15},PlotRange->{-0.2,1.2},
-				PlotLabel->Function,ImageSize->200]},
-		{Plot[fSigmoidal[A,Ka,-B,M,x],{x,0,15},PlotRange->{-0.2,1.2},
-				PlotLabel->Function,ImageSize->200]},
-		{Plot[fSigmoidal[A,Ka,B,M,x]+fSigmoidal[A,Ka,-B,M,x],{x,0,15},PlotRange->{-0.2,1.2},
-				PlotLabel->Function,ImageSize->200]}
-		}
-	],
-	{{A,0},0,1,.01},
-	{{Ka,1},0,1,.01},
-	{{B,1},0,10,.01},
-	{{M,8},0,15,.01}
-]
-
-
-fLeftAdditionSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))))+Ka/(1+E^(B1*(x-M1))); 
-(* Not the sign change in B1 terms*)
-fRightSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/(1+E^(B2*(x-(M1+L))));
-
-Manipulate[
-	Grid[
-		{
-		{Plot[{fLeftAdditionSigmoidal[Ka,B1,M1,B2,L,x],fRightSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Added Function",ImageSize->300]},
-		{Plot[{fLeftAdditionSigmoidal[Ka,B1,M1,B2,L,x]-fRightSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Difference Function",ImageSize->300]}
-		}
-	],
-
-	{{Ka,1},0,1,.01},
-	{{B1,1},0.01,10,.001},
-	{{M1,15},7.5-20,7.5+20,.01},
-	{{B2,2},0.01,10,0.001},
-	{{L,1},0,10,0.001}
-]
-
-
-fRightAdditionSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L)))))+Ka/(1+E^(-B2*(x-(M1+L)))); 
-(* Not the sign change in B2 terms*)
-fLeftSigmoidal[Ka_,B1_,M1_,B2_,L_,x_]=Ka/(1+E^(-B1*(x-M1)));
-
-Manipulate[
-	Grid[
-		{
-		{Plot[{fRightAdditionSigmoidal[Ka,B1,M1,B2,L,x],fLeftSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Added Function",ImageSize->300]},
-		{Plot[{fRightAdditionSigmoidal[Ka,B1,M1,B2,L,x]-fLeftSigmoidal[Ka,B1,M1,B2,L,x]},
-				{x,0,30},PlotRange->{-0.2,1.2},PlotLabel->"Difference Function",ImageSize->300]}
-		}
-	],
-
-	{{Ka,1},0,1,.01},
-	{{B1,1},0.01,10,.001},
-	{{M1,15},7.5-20,7.5+20,.01},
-	{{B2,2},0.01,10,0.001},
-	{{L,10},0,10,0.001}
-]
-
-
-fAdditionalSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-			(Ka/((1+E^(-B1*(x-M1)))*(1+E^(B2*(x-(M1+L))))))+((Ka*A2)/(1+E^(-B2*(x-(M1+L)))))+((Ka*A1)/(1+E^(B1*(x-M1))));
-
-Manipulate[
-	Plot[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},PlotRange->{0,1.2}],
-	{{A1,0},0,1,.01},
-	{{A2,0.2},0,1,.01},
-	{{Ka,1},0,1,.01},
-	{{B1,1},0,10,.01},
-	{{M1,8},0,20,.01},
-	{{B2,2},0,10,0.01},
-	{{L,10},0,10,0.001}
-]
-
-
-Limit[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->-\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-Limit[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],x->\[Infinity],Assumptions->{B1>0,B2>0,L>0}]
-
-
-Solve[D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0 ,x]
-Reduce[{D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-Solve[D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0,x]
-Reduce[{D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,2}]==0, 
-										B1>0,B2>0,L>0,Ka>A1,Ka>A2}, x, Reals]
-
-
-fDAdditionalSigmoidal[A1_,A2_,Ka_,B1_,M1_,B2_,L_,x_]=
-						D[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,1}];
-
-
-xValue=2000;
-
-Manipulate[
-
-	Grid[
-		{
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \(-\[Infinity]\)\)]\); it should be +1",
-									Sign[fDAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,-xValue]]]},
-		{StringForm["Sign of derivative is `` at \!\(\*SubscriptBox[\(Lim\), \(x \[Rule] \[Infinity]\)]\); it should be -1",
-									Sign[fDAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,xValue]]]},
-		{Plot[fAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-									PlotRange->{-0.2,2},PlotLabel->Function,ImageSize->350]},
-		{Plot[fDAdditionalSigmoidal[A1,A2,Ka,B1,M1,B2,L,x],{x,0,30},
-									PlotRange->Full,PlotLabel->Derivative,ImageSize->350]}
-		}
-	,Frame->All],
-
-	{{A1,0},0,1,.01},
-	{{A2,0.5368628},0,1,.01},
-	{{Ka,1.454867},0,2,.01},
-	{{B1,1.084971},0.01,10,.01},
-	{{M1,11.11337},7.5-20,7.5+20,.01},
-	{{B2,8.529749},0.01,10,0.01},
-	{{L,1.13329},0,10,0.001}
-]
-
-
-Plot[fAdditionalSigmoidal[0,1,1,8.88,19.5,0.121,0,x],{x,0,30},PlotRange->{0,1.2},ImageSize->350]
-
-
-A11=0; A22=.1; Kaa=1; B11=0.01; M11=10; B22=8.88; LL=1;
-
-Plot[1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL))))),{x,0,30},PlotRange->{-0.2,1.2},ImageSize->350]
-const=FindMaxValue[(1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL)))))),{x,M11+LL/2},
-						AccuracyGoal->200,PrecisionGoal->180,WorkingPrecision->210];
-
-fAdditionalSigmoidalPartA[x_]:=Kaa/const*1/((1+E^(-B11*(x-M11)))*(1+E^(B22*(x-(M11+LL)))))
-
-fAdditionalSigmoidalPartB1[x_]:=((Kaa*A22)/(const*(1+E^(-B22*(x-(M11+LL))))))-((Kaa*A22)/const-Kaa*A22)
-fAdditionalSigmoidalPartB2[x_]:=(fAdditionalSigmoidalPartB1[x]+Abs[fAdditionalSigmoidalPartB1[x]])/2
-
-fAdditionalSigmoidalPartC1[x_]:=((Kaa*A11)/(const*(1+E^(B11*(x-M11)))))-((Kaa*A11)/const-Kaa*A11)
-fAdditionalSigmoidalPartC2[x_]:=(fAdditionalSigmoidalPartC1[x]+Abs[fAdditionalSigmoidalPartC1[x]])/2
-
-fAdditionalSigmoidalN[x_]:=fAdditionalSigmoidalPartA[x]+fAdditionalSigmoidalPartB2[x]+fAdditionalSigmoidalPartC2[x]
-Plot[fAdditionalSigmoidalN[x],{x,0,30},PlotRange->{-0.2,2},ImageSize->350]
-
-
-
diff --git a/mathematica/simple toy model.nb b/mathematica/simple toy model.nb
deleted file mode 100644
index 87f16e2..0000000
--- a/mathematica/simple toy model.nb	
+++ /dev/null
@@ -1,229 +0,0 @@
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diff --git a/vignettes/double_sigmoidal_vignette.Rmd b/vignettes/double_sigmoidal_vignette.Rmd
index e853c5e..b885070 100644
--- a/vignettes/double_sigmoidal_vignette.Rmd
+++ b/vignettes/double_sigmoidal_vignette.Rmd
@@ -32,7 +32,6 @@ set.seed(seedNo)
 require("sicegar")
 require("dplyr")
 require("ggplot2")
-require("cowplot")
 ###*****************************
 ```
 
@@ -50,12 +49,12 @@ time=seq(3,24,0.5)
 noise_parameter=0.1
 intensity_noise=stats::runif(n = length(time),min = 0,max = 1)*noise_parameter
 intensity=doublesigmoidalFitFormula(time,
-                                   finalAsymptoteIntensity=.3,
-                                   maximum=4,
-                                   slope1=1,
-                                   midPoint1=7,
-                                   slope2=1,
-                                   midPointDistance=8)
+                                    finalAsymptoteIntensity=.3,
+                                    maximum=4,
+                                    slope1=1,
+                                    midPoint1=7,
+                                    slope2=1,
+                                    midPointDistance=8)
 intensity=intensity+intensity_noise
 
 dataInput=data.frame(intensity=intensity,time=time)
@@ -85,8 +84,8 @@ intensityData=intensityData/intensityRatio
 The normalization code is 
 
 ```{r normalize_data}
-normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
+normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
 ```
 
 
@@ -108,9 +107,9 @@ normalizedInput$timeIntensityData %>%
 dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined
 combined$process <- factor(combined$process, levels = c("raw","normalized"))
 
-ggplot(combined,aes(x=time, y=intensity))+
-  facet_wrap(~process, scales = "free")+
-  geom_point()
+ggplot2::ggplot(combined,aes(x=time, y=intensity))+
+  ggplot2::facet_wrap(~process, scales = "free")+
+  ggplot2::geom_point()
 ```
 
 ## Double-Sigmoidal fit of the data
@@ -118,7 +117,7 @@ ggplot(combined,aes(x=time, y=intensity))+
 Now it is time to calculate the parameters by using `sicegar::doublesigmoidalFitFunction()`
 
 ```{r doublesigmoidalfit_data}
-parameterVector<-doublesigmoidalFitFunction(normalizedInput,tryCounter=2)
+parameterVector<-sicegar::doublesigmoidalFitFunction(normalizedInput,tryCounter=2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
 
@@ -200,16 +199,16 @@ They are the parameters associated with parameter “finalAsymptoteIntensity”
 * `finalAsymptoteIntensity_Std_Error`
 * `finalAsymptoteIntensity_t_value`
 * `finalAsymptoteIntensity_Pr_t`
- 
+
 They are the parameters associated with the quality of the fit.
 
 * `residual_Sum_of_Squares`: Small value indicate better fit
 * `log_likelihood`: Higher value indicate a better fit
 * `AIC_value`: Smaller value indicate a better fit
 * `BIC_value`: Smaller value indicate a better fit
-  
+
 They are the fitted values after converting everything from normalized to un-normalized scale. (Without numeric correction)
-       
+
 * `maximum_Estimate`: Maximum intensity estimate for the raw data
 * `slope1_Estimate`: __Slope1 parameter__ estimate for the raw data 
 * `midPoint1_Estimate`: Mid-point 1 estimate (time the intensity reaches 1/2 of maximum) for the raw data. _Needs numerical correction_
@@ -223,20 +222,20 @@ By using the `maximum_Estimate`, `slope_Estimate`, `midPoint_Estimate` parameter
 
 ```{r plot raw data and fit, fig.height=4, fig.width=8}
 intensityTheoretical=
-       doublesigmoidalFitFormula(
-               time,
-               finalAsymptoteIntensity=parameterVector$finalAsymptoteIntensity_Estimate,
-               maximum=parameterVector$maximum_Estimate,
-               slope1=parameterVector$slope1_Estimate,
-               midPoint1=parameterVector$midPoint1_Estimate,
-               slope2=parameterVector$slope2_Estimate,
-               midPointDistance=parameterVector$midPointDistance_Estimate)
-
- comparisonData=cbind(dataInput,intensityTheoretical)
-
- require(ggplot2)
- ggplot(comparisonData)+
-   geom_point(aes(x=time, y=intensity))+
-   geom_line(aes(x=time,y=intensityTheoretical))+
-   expand_limits(x = 0, y = 0)
-```
\ No newline at end of file
+  sicegar::doublesigmoidalFitFormula(
+    time,
+    finalAsymptoteIntensity=parameterVector$finalAsymptoteIntensity_Estimate,
+    maximum=parameterVector$maximum_Estimate,
+    slope1=parameterVector$slope1_Estimate,
+    midPoint1=parameterVector$midPoint1_Estimate,
+    slope2=parameterVector$slope2_Estimate,
+    midPointDistance=parameterVector$midPointDistance_Estimate)
+
+comparisonData=cbind(dataInput,intensityTheoretical)
+
+require(ggplot2)
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
+```
diff --git a/vignettes/linear_vignette.Rmd b/vignettes/linear_vignette.Rmd
index 0db5e90..66dc47c 100644
--- a/vignettes/linear_vignette.Rmd
+++ b/vignettes/linear_vignette.Rmd
@@ -32,7 +32,6 @@ set.seed(seedNo)
 require("sicegar")
 require("dplyr")
 require("ggplot2")
-require("cowplot")
 ###*****************************
 ```
 
@@ -77,8 +76,8 @@ intensityData=intensityData/intensityRatio
 The normalization code is 
 
 ```{r normalize_data}
-normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
+normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
 ```
 
 
@@ -100,9 +99,9 @@ normalizedInput$timeIntensityData %>%
 dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined
 combined$process <- factor(combined$process, levels = c("raw","normalized"))
 
-ggplot(combined,aes(x=time, y=intensity))+
-  facet_wrap(~process, scales = "free")+
-  geom_point()
+ggplot2::ggplot(combined,aes(x=time, y=intensity))+
+  ggplot2::facet_wrap(~process, scales = "free")+
+  ggplot2::geom_point()
 ```
 
 ## Line fit of the data
@@ -110,7 +109,7 @@ ggplot(combined,aes(x=time, y=intensity))+
 Now it is time to calculate the parameters by using `sicegar::lineFitFunction()`
 
 ```{r linefit_data}
-parameterVector<-lineFitFunction(dataInput = normalizedInput, tryCounter = 2)
+parameterVector<-sicegar::lineFitFunction(dataInput = normalizedInput, tryCounter = 2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::lineFitFunction is called from sicegar::fitFunction. 
 
@@ -183,13 +182,13 @@ They are the fitted values after converting everything from normalized to un-nor
 By using the `intersection_Estimate`, `slope_Estimate` parameters of the linefit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::lineFitFormula()`. We can draw the best line on top of our initial data.
 
 ```{r plot raw data and fit, fig.height=4, fig.width=8}
-intensityTheoretical=lineFitFormula(time,
-                                    slope=parameterVector$slope_Estimate,
-                                    intersection=parameterVector$intersection_Estimate)
+intensityTheoretical=sicegar::lineFitFormula(time,
+                                             slope=parameterVector$slope_Estimate,
+                                             intersection=parameterVector$intersection_Estimate)
 comparisonData=cbind(dataInput,intensityTheoretical)
 
-ggplot(comparisonData)+
-  geom_point(aes(x=time, y=intensity))+
-  geom_line(aes(x=time,y=intensityTheoretical))+
-  expand_limits(x = 0, y = 0)
-```
\ No newline at end of file
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
+```
diff --git a/vignettes/sigmoidal_vignette.Rmd b/vignettes/sigmoidal_vignette.Rmd
index 07423fc..25bcc18 100644
--- a/vignettes/sigmoidal_vignette.Rmd
+++ b/vignettes/sigmoidal_vignette.Rmd
@@ -12,12 +12,12 @@ vignette: >
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
-
-# The Sigmoidal Fit Function
-This is a document invetigates details of sigmoidal model 
-
-
-```{r install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE}
+  
+  # The Sigmoidal Fit Function
+  This is a document invetigates details of sigmoidal model 
+  
+  
+  ```{r install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE}
 
 ###*****************************
 # INITIAL COMMANDS TO RESET THE SYSTEM
@@ -32,7 +32,6 @@ set.seed(seedNo)
 require("sicegar")
 require("dplyr")
 require("ggplot2")
-require("cowplot")
 ###*****************************
 ```
 
@@ -83,8 +82,8 @@ intensityData=intensityData/intensityRatio
 The normalization code is 
 
 ```{r normalize_data}
-normalizedInput = normalizeData(dataInput = dataInput, 
-                                dataInputName = "Sample001")
+normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
 ```
 
 
@@ -106,9 +105,9 @@ normalizedInput$timeIntensityData %>%
 dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined
 combined$process <- factor(combined$process, levels = c("raw","normalized"))
 
-ggplot(combined,aes(x=time, y=intensity))+
-  facet_wrap(~process, scales = "free")+
-  geom_point()
+ggplot2::ggplot(combined,aes(x=time, y=intensity))+
+  ggplot2::facet_wrap(~process, scales = "free")+
+  ggplot2::geom_point()
 ```
 
 ## Sigmoidal fit of the data
@@ -116,7 +115,7 @@ ggplot(combined,aes(x=time, y=intensity))+
 Now it is time to calculate the parameters by using `sicegar::sigmoidalFitFunction()`
 
 ```{r sigmoidalfit_data}
-parameterVector<-sigmoidalFitFunction(normalizedInput,tryCounter=2)
+parameterVector<-sicegar::sigmoidalFitFunction(normalizedInput,tryCounter=2)
 
 # Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
 
@@ -192,14 +191,14 @@ They are the fitted values after converting everything from normalized to un-nor
 By using the `maximum_Estimate`, `slope_Estimate`, `midPoint_Estimate` parameters of the sigmoidalfit and the time sequence that we already created we can calculate the intensity values by the help of `sicegar::sigmoidalFitFormula()`. We can draw the best sigmoidal fit on top of our initial data.
 
 ```{r plot raw data and fit, fig.height=4, fig.width=8}
-intensityTheoretical=sigmoidalFitFormula(time,
-                                        maximum=parameterVector$maximum_Estimate,
-                                        slope=parameterVector$slope_Estimate,
-                                        midPoint=parameterVector$midPoint_Estimate)
+intensityTheoretical=sicegar::sigmoidalFitFormula(time,
+                                                  maximum=parameterVector$maximum_Estimate,
+                                                  slope=parameterVector$slope_Estimate,
+                                                  midPoint=parameterVector$midPoint_Estimate)
 comparisonData=cbind(dataInput,intensityTheoretical)
 
-ggplot(comparisonData)+
- geom_point(aes(x=time, y=intensity))+
- geom_line(aes(x=time,y=intensityTheoretical))+
- expand_limits(x = 0, y = 0)
-```
\ No newline at end of file
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
+```