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changed a couple variable names
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chapter14.ipynb

Lines changed: 20 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -534,6 +534,8 @@
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" \"\"\"\n",
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" Return logistic regression parameters to identify best `psi`\n",
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" \n",
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" Note: this is written specifically for the problem in this program\n",
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" \n",
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" Paramters\n",
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" ---------\n",
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" psi : float\n",
@@ -556,7 +558,7 @@
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" data['H_of_psi'] = data.wt82_71 - psi * data.qsmk\n",
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" X = data[X_cols]\n",
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" \n",
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" gee = sm.GEE(y, X, groups=nhefs.seqn, weights=weights, family=sm.families.Binomial())\n",
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" gee = sm.GEE(y, X, groups=data.seqn, weights=weights, family=sm.families.Binomial())\n",
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" res = gee.fit()\n",
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" \n",
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" alpha = res.params.H_of_psi\n",
@@ -599,7 +601,9 @@
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"g_info = logit_g_info(3.446, nhefs, y, X_cols, ip_censor)"
@@ -643,7 +647,9 @@
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"g_info = [\n",
@@ -655,7 +661,9 @@
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{
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"cell_type": "code",
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"execution_count": 27,
658-
"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# by default, `min` will minimize the first value,\n",
@@ -698,7 +706,7 @@
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"data": {
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T5ZhxZKFgxl2P57qEuTmJLJ9jewn+6rMXzSA2Mpz/XGN7C6HEQsGMu+e2VnOo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\nPy1LIaBL8NN4AAAAAElFTkSuQmCC\n",
700708
"text/plain": [
701-
"<matplotlib.figure.Figure at 0x7f1e14ea9748>"
709+
"<matplotlib.figure.Figure at 0x7fd4b39b92b0>"
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]
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},
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"metadata": {},
@@ -843,11 +851,13 @@
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{
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"cell_type": "code",
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"execution_count": 34,
846-
"metadata": {},
854+
"metadata": {
855+
"collapsed": true
856+
},
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"outputs": [],
848858
"source": [
849859
"glm = sm.GLM(\n",
850-
" y,\n",
860+
" A,\n",
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" X,\n",
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" freq_weights=ip_censor,\n",
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" family=sm.families.Binomial()\n",
@@ -949,7 +959,9 @@
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{
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"cell_type": "code",
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"execution_count": 36,
952-
"metadata": {},
962+
"metadata": {
963+
"collapsed": true
964+
},
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"outputs": [],
954966
"source": [
955967
"A_pred = res.predict(X)\n",

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