testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import fastreg as fr\n",
    "from fastreg import I, R, C, D, B, factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = 'cpu' # 'gpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>yhat0</th>\n",
       "      <th>yhat</th>\n",
       "      <th>id1</th>\n",
       "      <th>id2</th>\n",
       "      <th>y0</th>\n",
       "      <th>y</th>\n",
       "      <th>Ep0</th>\n",
       "      <th>Ep</th>\n",
       "      <th>p0</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.429225</td>\n",
       "      <td>0.996297</td>\n",
       "      <td>0.269011</td>\n",
       "      <td>1.179011</td>\n",
       "      <td>G</td>\n",
       "      <td>31</td>\n",
       "      <td>0.366874</td>\n",
       "      <td>1.439623</td>\n",
       "      <td>1.308670</td>\n",
       "      <td>3.251157</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.687153</td>\n",
       "      <td>0.103194</td>\n",
       "      <td>0.368062</td>\n",
       "      <td>0.888062</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.166090</td>\n",
       "      <td>1.336905</td>\n",
       "      <td>1.444932</td>\n",
       "      <td>2.430415</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.764353</td>\n",
       "      <td>-0.485235</td>\n",
       "      <td>0.038165</td>\n",
       "      <td>0.488165</td>\n",
       "      <td>D</td>\n",
       "      <td>15</td>\n",
       "      <td>0.180055</td>\n",
       "      <td>0.442564</td>\n",
       "      <td>1.038903</td>\n",
       "      <td>1.629324</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.309887</td>\n",
       "      <td>0.752891</td>\n",
       "      <td>0.458768</td>\n",
       "      <td>0.688768</td>\n",
       "      <td>B</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.972028</td>\n",
       "      <td>0.847762</td>\n",
       "      <td>1.582124</td>\n",
       "      <td>1.991261</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.616042</td>\n",
       "      <td>1.812251</td>\n",
       "      <td>1.002538</td>\n",
       "      <td>2.222538</td>\n",
       "      <td>D</td>\n",
       "      <td>92</td>\n",
       "      <td>-0.449711</td>\n",
       "      <td>2.159141</td>\n",
       "      <td>2.725189</td>\n",
       "      <td>9.230728</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>1.560403</td>\n",
       "      <td>2.205245</td>\n",
       "      <td>1.891268</td>\n",
       "      <td>2.931268</td>\n",
       "      <td>E</td>\n",
       "      <td>64</td>\n",
       "      <td>2.478428</td>\n",
       "      <td>3.665472</td>\n",
       "      <td>6.627765</td>\n",
       "      <td>18.751385</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1.974411</td>\n",
       "      <td>1.783084</td>\n",
       "      <td>1.762174</td>\n",
       "      <td>2.402174</td>\n",
       "      <td>A</td>\n",
       "      <td>64</td>\n",
       "      <td>1.630717</td>\n",
       "      <td>2.421973</td>\n",
       "      <td>5.825088</td>\n",
       "      <td>11.047168</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>0.192736</td>\n",
       "      <td>1.049506</td>\n",
       "      <td>0.787524</td>\n",
       "      <td>2.197524</td>\n",
       "      <td>G</td>\n",
       "      <td>81</td>\n",
       "      <td>1.282229</td>\n",
       "      <td>2.073151</td>\n",
       "      <td>2.197948</td>\n",
       "      <td>9.002698</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>-0.431755</td>\n",
       "      <td>-0.296754</td>\n",
       "      <td>-0.207579</td>\n",
       "      <td>1.162421</td>\n",
       "      <td>F</td>\n",
       "      <td>87</td>\n",
       "      <td>0.740579</td>\n",
       "      <td>0.982307</td>\n",
       "      <td>0.812549</td>\n",
       "      <td>3.197667</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>-1.346914</td>\n",
       "      <td>0.770435</td>\n",
       "      <td>0.158187</td>\n",
       "      <td>1.308187</td>\n",
       "      <td>H</td>\n",
       "      <td>45</td>\n",
       "      <td>-1.555606</td>\n",
       "      <td>2.327482</td>\n",
       "      <td>1.171385</td>\n",
       "      <td>3.699461</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             x1        x2     yhat0      yhat id1  id2        y0         y  \\\n",
       "0     -1.429225  0.996297  0.269011  1.179011   G   31  0.366874  1.439623   \n",
       "1      0.687153  0.103194  0.368062  0.888062   F    2 -1.166090  1.336905   \n",
       "2      0.764353 -0.485235  0.038165  0.488165   D   15  0.180055  0.442564   \n",
       "3     -0.309887  0.752891  0.458768  0.688768   B   13 -0.972028  0.847762   \n",
       "4     -0.616042  1.812251  1.002538  2.222538   D   92 -0.449711  2.159141   \n",
       "...         ...       ...       ...       ...  ..  ...       ...       ...   \n",
       "99995  1.560403  2.205245  1.891268  2.931268   E   64  2.478428  3.665472   \n",
       "99996  1.974411  1.783084  1.762174  2.402174   A   64  1.630717  2.421973   \n",
       "99997  0.192736  1.049506  0.787524  2.197524   G   81  1.282229  2.073151   \n",
       "99998 -0.431755 -0.296754 -0.207579  1.162421   F   87  0.740579  0.982307   \n",
       "99999 -1.346914  0.770435  0.158187  1.308187   H   45 -1.555606  2.327482   \n",
       "\n",
       "            Ep0         Ep  p0   p  \n",
       "0      1.308670   3.251157   1   4  \n",
       "1      1.444932   2.430415   0   3  \n",
       "2      1.038903   1.629324   1   0  \n",
       "3      1.582124   1.991261   1   1  \n",
       "4      2.725189   9.230728   0  10  \n",
       "...         ...        ...  ..  ..  \n",
       "99995  6.627765  18.751385   4  13  \n",
       "99996  5.825088  11.047168   5  16  \n",
       "99997  2.197948   9.002698   2  11  \n",
       "99998  0.812549   3.197667   1   2  \n",
       "99999  1.171385   3.699461   1   6  \n",
       "\n",
       "[100000 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate random testing data\n",
    "data = fr.dataset(N=100_000, K1=10, K2=100, models=['linear', 'poisson'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formulas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Factor` — a column or expression\n",
    "\n",
    "`Term` — a combination of `Factor`s\n",
    "\n",
    "`Formula` — a combination of unique `Term`s\n",
    "\n",
    "Built in `Factor`s are: `[R]eal`, `[C]ategorical`, `[I]ntercept`, `[D]emeaned`, and `[B]inned`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R(x1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pure [R]eal (a factor)\n",
    "R('x1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R(x1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convenience method for simple columns\n",
    "R.x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R(x1)*R(x2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interacted [R]eals (a term)\n",
    "R.x1*R.x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I + R(x1)*R(x2) + C(id1)|FIRST"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add in [I]ntercept and [C]ategorical (a formula)\n",
    "I + R.x1*R.x2 + C.id1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C(x1:bin10|FIRST)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pure [B]inned column\n",
    "B('x1', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I + C(x1:bin10)*R(x1) + R(x2-μ|id1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add in [B]inned and [D]emeaned (a formula)\n",
    "I + B.x1(10)*R.x1 + D.x2('id1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I + R(abs(x1)) + C(x2 > 0)|FIRST"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use general python expressions in factors\n",
    "I + R('abs(x1)') + C('x2 > 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.073226</td>\n",
       "      <td>0.032487</td>\n",
       "      <td>0.009552</td>\n",
       "      <td>0.136899</td>\n",
       "      <td>0.024196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.297097</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.290881</td>\n",
       "      <td>0.303312</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.604929</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.598712</td>\n",
       "      <td>0.611145</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=B</th>\n",
       "      <td>0.131783</td>\n",
       "      <td>0.014253</td>\n",
       "      <td>0.103848</td>\n",
       "      <td>0.159719</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=C</th>\n",
       "      <td>0.230635</td>\n",
       "      <td>0.014157</td>\n",
       "      <td>0.202887</td>\n",
       "      <td>0.258383</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=95</th>\n",
       "      <td>0.943004</td>\n",
       "      <td>0.044338</td>\n",
       "      <td>0.856104</td>\n",
       "      <td>1.029905</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=96</th>\n",
       "      <td>0.976060</td>\n",
       "      <td>0.044757</td>\n",
       "      <td>0.888337</td>\n",
       "      <td>1.063783</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=97</th>\n",
       "      <td>0.951987</td>\n",
       "      <td>0.044315</td>\n",
       "      <td>0.865131</td>\n",
       "      <td>1.038842</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=98</th>\n",
       "      <td>0.908992</td>\n",
       "      <td>0.043913</td>\n",
       "      <td>0.822924</td>\n",
       "      <td>0.995060</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=99</th>\n",
       "      <td>0.977369</td>\n",
       "      <td>0.044651</td>\n",
       "      <td>0.889854</td>\n",
       "      <td>1.064884</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "y          coeff    stderr     low95    high95    pvalue\n",
       "I       0.073226  0.032487  0.009552  0.136899  0.024196\n",
       "x1      0.297097  0.003171  0.290881  0.303312  0.000000\n",
       "x2      0.604929  0.003172  0.598712  0.611145  0.000000\n",
       "id1=B   0.131783  0.014253  0.103848  0.159719  0.000000\n",
       "id1=C   0.230635  0.014157  0.202887  0.258383  0.000000\n",
       "...          ...       ...       ...       ...       ...\n",
       "id2=95  0.943004  0.044338  0.856104  1.029905  0.000000\n",
       "id2=96  0.976060  0.044757  0.888337  1.063783  0.000000\n",
       "id2=97  0.951987  0.044315  0.865131  1.038842  0.000000\n",
       "id2=98  0.908992  0.043913  0.822924  0.995060  0.000000\n",
       "id2=99  0.977369  0.044651  0.889854  1.064884  0.000000\n",
       "\n",
       "[111 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pythonic formula\n",
    "fr.ols(y=R.y, x=I+R.x1+R.x2+C.id1+C.id2, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.073226</td>\n",
       "      <td>0.032487</td>\n",
       "      <td>0.009552</td>\n",
       "      <td>0.136899</td>\n",
       "      <td>0.024196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.297097</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.290881</td>\n",
       "      <td>0.303312</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.604929</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.598712</td>\n",
       "      <td>0.611145</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=B</th>\n",
       "      <td>0.131783</td>\n",
       "      <td>0.014253</td>\n",
       "      <td>0.103848</td>\n",
       "      <td>0.159719</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=C</th>\n",
       "      <td>0.230635</td>\n",
       "      <td>0.014157</td>\n",
       "      <td>0.202887</td>\n",
       "      <td>0.258383</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=95</th>\n",
       "      <td>0.943004</td>\n",
       "      <td>0.044338</td>\n",
       "      <td>0.856104</td>\n",
       "      <td>1.029905</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=96</th>\n",
       "      <td>0.976060</td>\n",
       "      <td>0.044757</td>\n",
       "      <td>0.888337</td>\n",
       "      <td>1.063783</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=97</th>\n",
       "      <td>0.951987</td>\n",
       "      <td>0.044315</td>\n",
       "      <td>0.865131</td>\n",
       "      <td>1.038842</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=98</th>\n",
       "      <td>0.908992</td>\n",
       "      <td>0.043913</td>\n",
       "      <td>0.822924</td>\n",
       "      <td>0.995060</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=99</th>\n",
       "      <td>0.977369</td>\n",
       "      <td>0.044651</td>\n",
       "      <td>0.889854</td>\n",
       "      <td>1.064884</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "y          coeff    stderr     low95    high95    pvalue\n",
       "I       0.073226  0.032487  0.009552  0.136899  0.024196\n",
       "x1      0.297097  0.003171  0.290881  0.303312  0.000000\n",
       "x2      0.604929  0.003172  0.598712  0.611145  0.000000\n",
       "id1=B   0.131783  0.014253  0.103848  0.159719  0.000000\n",
       "id1=C   0.230635  0.014157  0.202887  0.258383  0.000000\n",
       "...          ...       ...       ...       ...       ...\n",
       "id2=95  0.943004  0.044338  0.856104  1.029905  0.000000\n",
       "id2=96  0.976060  0.044757  0.888337  1.063783  0.000000\n",
       "id2=97  0.951987  0.044315  0.865131  1.038842  0.000000\n",
       "id2=98  0.908992  0.043913  0.822924  0.995060  0.000000\n",
       "id2=99  0.977369  0.044651  0.889854  1.064884  0.000000\n",
       "\n",
       "[111 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R style formula\n",
    "form = 'y ~ 1 + x1 + x2 + C(id1) + C(id2)'\n",
    "fr.ols(formula=form, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.073226</td>\n",
       "      <td>0.032487</td>\n",
       "      <td>0.009552</td>\n",
       "      <td>0.136899</td>\n",
       "      <td>0.024196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.297097</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.290881</td>\n",
       "      <td>0.303312</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.604929</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.598712</td>\n",
       "      <td>0.611145</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=B</th>\n",
       "      <td>0.131783</td>\n",
       "      <td>0.014253</td>\n",
       "      <td>0.103848</td>\n",
       "      <td>0.159719</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=C</th>\n",
       "      <td>0.230635</td>\n",
       "      <td>0.014157</td>\n",
       "      <td>0.202887</td>\n",
       "      <td>0.258383</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=95</th>\n",
       "      <td>0.943004</td>\n",
       "      <td>0.044338</td>\n",
       "      <td>0.856104</td>\n",
       "      <td>1.029905</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=96</th>\n",
       "      <td>0.976060</td>\n",
       "      <td>0.044757</td>\n",
       "      <td>0.888337</td>\n",
       "      <td>1.063783</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=97</th>\n",
       "      <td>0.951987</td>\n",
       "      <td>0.044315</td>\n",
       "      <td>0.865131</td>\n",
       "      <td>1.038842</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=98</th>\n",
       "      <td>0.908992</td>\n",
       "      <td>0.043913</td>\n",
       "      <td>0.822924</td>\n",
       "      <td>0.995060</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=99</th>\n",
       "      <td>0.977369</td>\n",
       "      <td>0.044651</td>\n",
       "      <td>0.889854</td>\n",
       "      <td>1.064884</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "y          coeff    stderr     low95    high95    pvalue\n",
       "1       0.073226  0.032487  0.009552  0.136899  0.024196\n",
       "x1      0.297097  0.003171  0.290881  0.303312  0.000000\n",
       "x2      0.604929  0.003172  0.598712  0.611145  0.000000\n",
       "id1=B   0.131783  0.014253  0.103848  0.159719  0.000000\n",
       "id1=C   0.230635  0.014157  0.202887  0.258383  0.000000\n",
       "...          ...       ...       ...       ...       ...\n",
       "id2=95  0.943004  0.044338  0.856104  1.029905  0.000000\n",
       "id2=96  0.976060  0.044757  0.888337  1.063783  0.000000\n",
       "id2=97  0.951987  0.044315  0.865131  1.038842  0.000000\n",
       "id2=98  0.908992  0.043913  0.822924  0.995060  0.000000\n",
       "id2=99  0.977369  0.044651  0.889854  1.064884  0.000000\n",
       "\n",
       "[111 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tuple/list style formula\n",
    "fr.ols(y='y', x=['1', 'x1', 'x2', C('id1'), C('id2')], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.045507</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>1.038791</td>\n",
       "      <td>1.052223</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1-μ</th>\n",
       "      <td>0.296732</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.290007</td>\n",
       "      <td>0.303457</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2-μ|id1</th>\n",
       "      <td>0.605145</td>\n",
       "      <td>0.003432</td>\n",
       "      <td>0.598418</td>\n",
       "      <td>0.611872</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y            coeff    stderr     low95    high95  pvalue\n",
       "I         1.045507  0.003426  1.038791  1.052223     0.0\n",
       "x1-μ      0.296732  0.003431  0.290007  0.303457     0.0\n",
       "x2-μ|id1  0.605145  0.003432  0.598418  0.611872     0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# demeaned in action (x1 unconditional, x2 conditional on id1)\n",
    "fr.ols(y=R.y, x=I+D.x1+D.x2('id1'), data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.050414</td>\n",
       "      <td>0.008411</td>\n",
       "      <td>1.033929</td>\n",
       "      <td>1.066899</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.296694</td>\n",
       "      <td>0.003430</td>\n",
       "      <td>0.289972</td>\n",
       "      <td>0.303417</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=0)*x2</th>\n",
       "      <td>0.666943</td>\n",
       "      <td>0.040644</td>\n",
       "      <td>0.587283</td>\n",
       "      <td>0.746603</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=1)*x2</th>\n",
       "      <td>0.600625</td>\n",
       "      <td>0.015680</td>\n",
       "      <td>0.569892</td>\n",
       "      <td>0.631357</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=2)*x2</th>\n",
       "      <td>0.599660</td>\n",
       "      <td>0.009415</td>\n",
       "      <td>0.581206</td>\n",
       "      <td>0.618113</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=3)*x2</th>\n",
       "      <td>0.616892</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>0.596967</td>\n",
       "      <td>0.636817</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=4)*x2</th>\n",
       "      <td>0.615962</td>\n",
       "      <td>0.021202</td>\n",
       "      <td>0.574407</td>\n",
       "      <td>0.657516</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=5)*x2</th>\n",
       "      <td>0.614427</td>\n",
       "      <td>0.022103</td>\n",
       "      <td>0.571105</td>\n",
       "      <td>0.657749</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=6)*x2</th>\n",
       "      <td>0.596445</td>\n",
       "      <td>0.010333</td>\n",
       "      <td>0.576194</td>\n",
       "      <td>0.616697</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=7)*x2</th>\n",
       "      <td>0.606194</td>\n",
       "      <td>0.009389</td>\n",
       "      <td>0.587792</td>\n",
       "      <td>0.624597</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=8)*x2</th>\n",
       "      <td>0.594957</td>\n",
       "      <td>0.015061</td>\n",
       "      <td>0.565438</td>\n",
       "      <td>0.624476</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(x2:bin10=9)*x2</th>\n",
       "      <td>0.619874</td>\n",
       "      <td>0.043847</td>\n",
       "      <td>0.533935</td>\n",
       "      <td>0.705812</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y                   coeff    stderr     low95    high95  pvalue\n",
       "I                1.050414  0.008411  1.033929  1.066899     0.0\n",
       "x1               0.296694  0.003430  0.289972  0.303417     0.0\n",
       "(x2:bin10=0)*x2  0.666943  0.040644  0.587283  0.746603     0.0\n",
       "(x2:bin10=1)*x2  0.600625  0.015680  0.569892  0.631357     0.0\n",
       "(x2:bin10=2)*x2  0.599660  0.009415  0.581206  0.618113     0.0\n",
       "(x2:bin10=3)*x2  0.616892  0.010166  0.596967  0.636817     0.0\n",
       "(x2:bin10=4)*x2  0.615962  0.021202  0.574407  0.657516     0.0\n",
       "(x2:bin10=5)*x2  0.614427  0.022103  0.571105  0.657749     0.0\n",
       "(x2:bin10=6)*x2  0.596445  0.010333  0.576194  0.616697     0.0\n",
       "(x2:bin10=7)*x2  0.606194  0.009389  0.587792  0.624597     0.0\n",
       "(x2:bin10=8)*x2  0.594957  0.015061  0.565438  0.624476     0.0\n",
       "(x2:bin10=9)*x2  0.619874  0.043847  0.533935  0.705812     0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quantile binning (x2 on deciles, self interacted)\n",
    "fr.ols(y=R.y, x=I+R.x1+B.x2(10)*R.x2, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.028447</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>-0.161227</td>\n",
       "      <td>0.218122</td>\n",
       "      <td>0.768793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.296764</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>0.290521</td>\n",
       "      <td>0.303008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.605099</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.598854</td>\n",
       "      <td>0.611345</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=A,id2=1</th>\n",
       "      <td>0.175028</td>\n",
       "      <td>0.139597</td>\n",
       "      <td>-0.098578</td>\n",
       "      <td>0.448634</td>\n",
       "      <td>0.209912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=A,id2=2</th>\n",
       "      <td>0.043070</td>\n",
       "      <td>0.141511</td>\n",
       "      <td>-0.234286</td>\n",
       "      <td>0.320426</td>\n",
       "      <td>0.760856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=J,id2=95</th>\n",
       "      <td>1.910192</td>\n",
       "      <td>0.141916</td>\n",
       "      <td>1.632043</td>\n",
       "      <td>2.188342</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=J,id2=96</th>\n",
       "      <td>1.927507</td>\n",
       "      <td>0.136862</td>\n",
       "      <td>1.659261</td>\n",
       "      <td>2.195752</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=J,id2=97</th>\n",
       "      <td>1.844808</td>\n",
       "      <td>0.145444</td>\n",
       "      <td>1.559742</td>\n",
       "      <td>2.129874</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=J,id2=98</th>\n",
       "      <td>1.869408</td>\n",
       "      <td>0.133902</td>\n",
       "      <td>1.606965</td>\n",
       "      <td>2.131851</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=J,id2=99</th>\n",
       "      <td>1.969233</td>\n",
       "      <td>0.137182</td>\n",
       "      <td>1.700361</td>\n",
       "      <td>2.238104</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1002 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "y                coeff    stderr     low95    high95    pvalue\n",
       "I             0.028447  0.096774 -0.161227  0.218122  0.768793\n",
       "x1            0.296764  0.003185  0.290521  0.303008  0.000000\n",
       "x2            0.605099  0.003187  0.598854  0.611345  0.000000\n",
       "id1=A,id2=1   0.175028  0.139597 -0.098578  0.448634  0.209912\n",
       "id1=A,id2=2   0.043070  0.141511 -0.234286  0.320426  0.760856\n",
       "...                ...       ...       ...       ...       ...\n",
       "id1=J,id2=95  1.910192  0.141916  1.632043  2.188342  0.000000\n",
       "id1=J,id2=96  1.927507  0.136862  1.659261  2.195752  0.000000\n",
       "id1=J,id2=97  1.844808  0.145444  1.559742  2.129874  0.000000\n",
       "id1=J,id2=98  1.869408  0.133902  1.606965  2.131851  0.000000\n",
       "id1=J,id2=99  1.969233  0.137182  1.700361  2.238104  0.000000\n",
       "\n",
       "[1002 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hdfe — fast for one categorical term\n",
    "fr.ols(y=R.y, x=I+R.x1+R.x2, hdfe=C.id1*C.id2, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.046833</td>\n",
       "      <td>0.002910</td>\n",
       "      <td>1.041130</td>\n",
       "      <td>1.052536</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.297097</td>\n",
       "      <td>0.003209</td>\n",
       "      <td>0.290808</td>\n",
       "      <td>0.303385</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.604929</td>\n",
       "      <td>0.003069</td>\n",
       "      <td>0.598913</td>\n",
       "      <td>0.610944</td>\n",
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      "text/plain": [
       "y      coeff    stderr     low95    high95  pvalue\n",
       "I   1.046833  0.002910  1.041130  1.052536     0.0\n",
       "x1  0.297097  0.003209  0.290808  0.303385     0.0\n",
       "x2  0.604929  0.003069  0.598913  0.610944     0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absorbtion — fast but no std errors\n",
    "fr.ols(y=R.y, x=I+R.x1+R.x2, absorb=C.id1+C.id2, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.073226</td>\n",
       "      <td>0.015703</td>\n",
       "      <td>0.042448</td>\n",
       "      <td>0.104003</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.297097</td>\n",
       "      <td>0.003209</td>\n",
       "      <td>0.290808</td>\n",
       "      <td>0.303386</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.604929</td>\n",
       "      <td>0.003069</td>\n",
       "      <td>0.598913</td>\n",
       "      <td>0.610944</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=B</th>\n",
       "      <td>0.131783</td>\n",
       "      <td>0.013160</td>\n",
       "      <td>0.105990</td>\n",
       "      <td>0.157577</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=C</th>\n",
       "      <td>0.230635</td>\n",
       "      <td>0.012046</td>\n",
       "      <td>0.207024</td>\n",
       "      <td>0.254246</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=95</th>\n",
       "      <td>0.943004</td>\n",
       "      <td>0.027624</td>\n",
       "      <td>0.888863</td>\n",
       "      <td>0.997146</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=96</th>\n",
       "      <td>0.976060</td>\n",
       "      <td>0.027493</td>\n",
       "      <td>0.922174</td>\n",
       "      <td>1.029945</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=97</th>\n",
       "      <td>0.951987</td>\n",
       "      <td>0.030078</td>\n",
       "      <td>0.893035</td>\n",
       "      <td>1.010939</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=98</th>\n",
       "      <td>0.908992</td>\n",
       "      <td>0.025788</td>\n",
       "      <td>0.858449</td>\n",
       "      <td>0.959535</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=99</th>\n",
       "      <td>0.977369</td>\n",
       "      <td>0.028934</td>\n",
       "      <td>0.920659</td>\n",
       "      <td>1.034078</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "y          coeff    stderr     low95    high95    pvalue\n",
       "I       0.073226  0.015703  0.042448  0.104003  0.000003\n",
       "x1      0.297097  0.003209  0.290808  0.303386  0.000000\n",
       "x2      0.604929  0.003069  0.598913  0.610944  0.000000\n",
       "id1=B   0.131783  0.013160  0.105990  0.157577  0.000000\n",
       "id1=C   0.230635  0.012046  0.207024  0.254246  0.000000\n",
       "...          ...       ...       ...       ...       ...\n",
       "id2=95  0.943004  0.027624  0.888863  0.997146  0.000000\n",
       "id2=96  0.976060  0.027493  0.922174  1.029945  0.000000\n",
       "id2=97  0.951987  0.030078  0.893035  1.010939  0.000000\n",
       "id2=98  0.908992  0.025788  0.858449  0.959535  0.000000\n",
       "id2=99  0.977369  0.028934  0.920659  1.034078  0.000000\n",
       "\n",
       "[111 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clustered standard errors (any term allowed)\n",
    "fr.ols(y=R.y, x=I+R.x1+R.x2+C.id1+C.id2, cluster=C.id1*C.id2, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0] ℓ=1.19443, g=1.39613, Δβ=0.57061, Δℓ=inf, μR=0.50728, μC=0.47462\n",
      "[220] ℓ=2.52443, g=0.00047, Δβ=0.00007, Δℓ=0.00000, μR=0.33174, μC=0.50312\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>p</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.093779</td>\n",
       "      <td>0.021545</td>\n",
       "      <td>0.051552</td>\n",
       "      <td>0.136005</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.301392</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>0.298184</td>\n",
       "      <td>0.304601</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.600059</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.596872</td>\n",
       "      <td>0.603247</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=B</th>\n",
       "      <td>0.111922</td>\n",
       "      <td>0.009210</td>\n",
       "      <td>0.093871</td>\n",
       "      <td>0.129974</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=C</th>\n",
       "      <td>0.195763</td>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.178330</td>\n",
       "      <td>0.213196</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=95</th>\n",
       "      <td>0.929097</td>\n",
       "      <td>0.024111</td>\n",
       "      <td>0.881841</td>\n",
       "      <td>0.976354</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=96</th>\n",
       "      <td>0.984229</td>\n",
       "      <td>0.024382</td>\n",
       "      <td>0.936441</td>\n",
       "      <td>1.032018</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=97</th>\n",
       "      <td>0.959325</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>0.911379</td>\n",
       "      <td>1.007272</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=98</th>\n",
       "      <td>0.992712</td>\n",
       "      <td>0.024214</td>\n",
       "      <td>0.945254</td>\n",
       "      <td>1.040170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=99</th>\n",
       "      <td>1.017918</td>\n",
       "      <td>0.023985</td>\n",
       "      <td>0.970908</td>\n",
       "      <td>1.064927</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "p          coeff    stderr     low95    high95    pvalue\n",
       "I       0.093779  0.021545  0.051552  0.136005  0.000013\n",
       "x1      0.301392  0.001637  0.298184  0.304601  0.000000\n",
       "x2      0.600059  0.001626  0.596872  0.603247  0.000000\n",
       "id1=B   0.111922  0.009210  0.093871  0.129974  0.000000\n",
       "id1=C   0.195763  0.008894  0.178330  0.213196  0.000000\n",
       "...          ...       ...       ...       ...       ...\n",
       "id2=95  0.929097  0.024111  0.881841  0.976354  0.000000\n",
       "id2=96  0.984229  0.024382  0.936441  1.032018  0.000000\n",
       "id2=97  0.959325  0.024463  0.911379  1.007272  0.000000\n",
       "id2=98  0.992712  0.024214  0.945254  1.040170  0.000000\n",
       "id2=99  1.017918  0.023985  0.970908  1.064927  0.000000\n",
       "\n",
       "[111 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr.poisson(y=R.p, x=I+R.x1+R.x2+C.id1+C.id2, data=data, backend=backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0] ℓ=1.19443, g=1.39613, Δβ=0.57061, Δℓ=inf, μR=0.50728, μC=0.47462\n",
      "[220] ℓ=2.52443, g=0.00047, Δβ=0.00007, Δℓ=0.00000, μR=0.33174, μC=0.50312\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>p</th>\n",
       "      <th>coeff</th>\n",
       "      <th>stderr</th>\n",
       "      <th>low95</th>\n",
       "      <th>high95</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.093779</td>\n",
       "      <td>0.021544</td>\n",
       "      <td>0.051552</td>\n",
       "      <td>0.136005</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>0.301392</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>0.298184</td>\n",
       "      <td>0.304601</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.600059</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.596872</td>\n",
       "      <td>0.603247</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=B</th>\n",
       "      <td>0.111922</td>\n",
       "      <td>0.009210</td>\n",
       "      <td>0.093871</td>\n",
       "      <td>0.129974</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id1=C</th>\n",
       "      <td>0.195763</td>\n",
       "      <td>0.008894</td>\n",
       "      <td>0.178330</td>\n",
       "      <td>0.213196</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=95</th>\n",
       "      <td>0.929097</td>\n",
       "      <td>0.024111</td>\n",
       "      <td>0.881841</td>\n",
       "      <td>0.976354</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=96</th>\n",
       "      <td>0.984229</td>\n",
       "      <td>0.024382</td>\n",
       "      <td>0.936441</td>\n",
       "      <td>1.032017</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=97</th>\n",
       "      <td>0.959325</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>0.911379</td>\n",
       "      <td>1.007272</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=98</th>\n",
       "      <td>0.992712</td>\n",
       "      <td>0.024214</td>\n",
       "      <td>0.945254</td>\n",
       "      <td>1.040170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id2=99</th>\n",
       "      <td>1.017918</td>\n",
       "      <td>0.023985</td>\n",
       "      <td>0.970908</td>\n",
       "      <td>1.064927</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "p          coeff    stderr     low95    high95    pvalue\n",
       "I       0.093779  0.021544  0.051552  0.136005  0.000013\n",
       "x1      0.301392  0.001637  0.298184  0.304601  0.000000\n",
       "x2      0.600059  0.001626  0.596872  0.603247  0.000000\n",
       "id1=B   0.111922  0.009210  0.093871  0.129974  0.000000\n",
       "id1=C   0.195763  0.008894  0.178330  0.213196  0.000000\n",
       "...          ...       ...       ...       ...       ...\n",
       "id2=95  0.929097  0.024111  0.881841  0.976354  0.000000\n",
       "id2=96  0.984229  0.024382  0.936441  1.032017  0.000000\n",
       "id2=97  0.959325  0.024463  0.911379  1.007272  0.000000\n",
       "id2=98  0.992712  0.024214  0.945254  1.040170  0.000000\n",
       "id2=99  1.017918  0.023985  0.970908  1.064927  0.000000\n",
       "\n",
       "[111 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr.poisson(y=R.p, x=I+R.x1+R.x2+C.id1, hdfe=C.id2, data=data, backend=backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 450,
       "width": 550
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how do we do on ids?\n",
    "fr.testing.test_ols(data, plot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0] ℓ=1.19443, g=1.39613, Δβ=0.57061, Δℓ=inf, μR=0.50728, μC=0.47462\n",
      "[220] ℓ=2.52443, g=0.00047, Δβ=0.00007, Δℓ=0.00000, μR=0.33174, μC=0.50312\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 450,
       "width": 550
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# slow on first run, try it twice\n",
    "fr.testing.test_glm(data, plot=True, backend=backend);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R(demean(x1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Column(name='demean(x1)', labels='demean(x1)', values=array([-1.42976817,  0.68660894,  0.76380937, ...,  0.19219221,\n",
       "       -0.43229829, -1.34745765]), valid=array([ True,  True,  True, ...,  True,  True,  True]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simple factor\n",
    "@factor\n",
    "def demean(x):\n",
    "    return x - np.mean(x)\n",
    "fact = demean.x1\n",
    "print(fact)\n",
    "fact.eval(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R(cdemean(x1,id1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Column(name='cdemean(x1,id1)', labels='cdemean(x1,id1)', values=array([-1.43336595,  0.6757273 ,  0.74892574, ...,  0.18859444,\n",
       "       -0.44317993, -1.33460797]), valid=array([ True,  True,  True, ...,  True,  True,  True]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# complex factor\n",
    "@factor(eval_args=(0, 1))\n",
    "def cdemean(x, i):\n",
    "    datf = pd.DataFrame({'vals': x, 'cond': i})\n",
    "    cmean = datf.groupby('cond')['vals'].mean().rename('mean')\n",
    "    datf = datf.join(cmean, on='cond')\n",
    "    return datf['vals'] - datf['mean']\n",
    "cfact = cdemean('x1', 'id1')\n",
    "print(cfact)\n",
    "cfact.eval(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R(x1-μ)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Column(name='x1-μ', labels='x1-μ', values=array([-1.42976817,  0.68660894,  0.76380937, ...,  0.19219221,\n",
       "       -0.43229829, -1.34745765]), valid=array([ True,  True,  True, ...,  True,  True,  True]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# named factor\n",
    "@factor(name=lambda x: f'{x}-μ')\n",
    "def ndemean(x):\n",
    "    return x - np.mean(x)\n",
    "nfact = ndemean.x1\n",
    "print(nfact)\n",
    "nfact.eval(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R(@logit(x1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Column(name='@logit(x1)', labels='@logit(x1)', values=array([0.19321953, 0.6653332 , 0.68229806, ..., 0.54803535, 0.39370741,\n",
       "       0.20637534]), valid=array([ True,  True,  True, ...,  True,  True,  True]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inject into eval namespace (note @ required to reference)\n",
    "logit = lambda x: 1/(1+np.exp(-x))\n",
    "lfact = R('@logit(x1)')\n",
    "print(lfact)\n",
    "lfact.eval(data, extern={'logit': logit})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R(logit(x1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Column(name='logit(x1)', labels='logit(x1)', values=array([0.19321953, 0.6653332 , 0.68229806, ..., 0.54803535, 0.39370741,\n",
       "       0.20637534]), valid=array([ True,  True,  True, ...,  True,  True,  True]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create custom factor (somewhat more elegant)\n",
    "flogit = factor(logit, name='logit')\n",
    "lfact = flogit('x1')\n",
    "print(lfact)\n",
    "lfact.eval(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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