Skip to content

Instantly share code, notes, and snippets.

@fonnesbeck

fonnesbeck/visit_weight_analysis.ipynb

Created October 31, 2018 15:36
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save fonnesbeck/26d5fb697524c37bcfeb1ec33e0fbb4a to your computer and use it in GitHub Desktop.
Save fonnesbeck/26d5fb697524c37bcfeb1ec33e0fbb4a to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
visit_weight_analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"sns.set(style='ticks')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"DATA_PATH = '../data/NCS/'\n",
"\n",
"teaching_child = pd.read_csv(DATA_PATH + 'ncs_teaching_child_v1_1.csv', \n",
" index_col=0, na_values=['M'])\n",
"teaching_childhealth = pd.read_csv(DATA_PATH + 'ncs_teaching_childhealth_v1.csv',\n",
" na_values=['M'])\n",
"teaching_mompreghealth = pd.read_csv(DATA_PATH + 'ncs_teaching_mompreghealth_v1.csv',\n",
" index_col=0, na_values=['M'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"teaching_childhealth.CHILD_AGE.hist(bins=30);"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"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></th>\n",
" <th>HEALTH</th>\n",
" <th>BMI</th>\n",
" <th>BMI_CAT</th>\n",
" <th>THYROID</th>\n",
" <th>HIGHBP_NOTPREG</th>\n",
" <th>ASTHMA</th>\n",
" <th>DIABETES</th>\n",
" <th>HIGHBP_PREG</th>\n",
" <th>PREECLAMPSIA</th>\n",
" <th>EARLY_LABOR</th>\n",
" <th>...</th>\n",
" <th>RH_DISEASE</th>\n",
" <th>URINE</th>\n",
" <th>VAGINOSIS</th>\n",
" <th>GROUP_B</th>\n",
" <th>CIG_NOW</th>\n",
" <th>MOM_PIDX</th>\n",
" <th>CHILD_SEX</th>\n",
" <th>GESTATIONAL_AGE</th>\n",
" <th>CHILD_PIDX</th>\n",
" <th>VISIT_WT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9589</th>\n",
" <td>1.0</td>\n",
" <td>22.0</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>b00014490</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>a65385688</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10207</th>\n",
" <td>1.0</td>\n",
" <td>22.9</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>b00028364</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>a69997363</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4943</th>\n",
" <td>2.0</td>\n",
" <td>24.8</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>b00048093</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>a33612802</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4604</th>\n",
" <td>2.0</td>\n",
" <td>37.8</td>\n",
" <td>5.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>b00060642</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>a31316804</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13800</th>\n",
" <td>1.0</td>\n",
" <td>28.0</td>\n",
" <td>3.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>b00096696</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>a95202738</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" HEALTH BMI BMI_CAT THYROID HIGHBP_NOTPREG ASTHMA DIABETES \\\n",
"9589 1.0 22.0 2.0 0 0 0 0 \n",
"10207 1.0 22.9 2.0 0 0 0 0 \n",
"4943 2.0 24.8 2.0 0 0 0 0 \n",
"4604 2.0 37.8 5.0 0 0 0 0 \n",
"13800 1.0 28.0 3.0 0 0 0 0 \n",
"\n",
" HIGHBP_PREG PREECLAMPSIA EARLY_LABOR ... RH_DISEASE URINE \\\n",
"9589 0 0 0 ... 0 0 \n",
"10207 0 0 0 ... 0 0 \n",
"4943 0 0 0 ... 0 0 \n",
"4604 0 0 0 ... 0 0 \n",
"13800 0 0 0 ... 0 0 \n",
"\n",
" VAGINOSIS GROUP_B CIG_NOW MOM_PIDX CHILD_SEX GESTATIONAL_AGE \\\n",
"9589 0 0 0 b00014490 2.0 4.0 \n",
"10207 0 0 0 b00028364 1.0 4.0 \n",
"4943 0 0 0 b00048093 2.0 4.0 \n",
"4604 0 0 0 b00060642 1.0 4.0 \n",
"13800 0 0 0 b00096696 2.0 4.0 \n",
"\n",
" CHILD_PIDX VISIT_WT \n",
"9589 a65385688 16.0 \n",
"10207 a69997363 NaN \n",
"4943 a33612802 17.0 \n",
"4604 a31316804 16.0 \n",
"13800 a95202738 14.0 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wt_under_10m = teaching_childhealth.loc[teaching_childhealth.CHILD_AGE<10, ['CHILD_PIDX', 'VISIT_WT']]\n",
"child_data = teaching_child[['MOM_PIDX', 'CHILD_SEX', 'GESTATIONAL_AGE']].merge(wt_under_10m, left_index=True, right_on='CHILD_PIDX')\n",
"data_merged = teaching_mompreghealth.merge(child_data, left_index=True, right_on='MOM_PIDX')\n",
"data_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"HEALTH 0.040450\n",
"BMI 0.185016\n",
"BMI_CAT 0.185016\n",
"THYROID 0.000000\n",
"HIGHBP_NOTPREG 0.000000\n",
"ASTHMA 0.000000\n",
"DIABETES 0.000000\n",
"HIGHBP_PREG 0.000000\n",
"PREECLAMPSIA 0.000000\n",
"EARLY_LABOR 0.000000\n",
"ANEMIA 0.000000\n",
"KIDNEY 0.000000\n",
"NAUSEA 0.000000\n",
"RH_DISEASE 0.000000\n",
"URINE 0.000000\n",
"VAGINOSIS 0.000000\n",
"GROUP_B 0.000000\n",
"CIG_NOW 0.000000\n",
"MOM_PIDX 0.000000\n",
"CHILD_SEX 0.000000\n",
"GESTATIONAL_AGE 0.008442\n",
"CHILD_PIDX 0.000000\n",
"VISIT_WT 0.220190\n",
"dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_merged.isnull().mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I will try to fit the following model, which contains both child and mother attributes:\n",
"\n",
" visit_weight ~ child_sex + gest_age + mom_bmi + mom_health"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"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></th>\n",
" <th>VISIT_WT</th>\n",
" <th>CHILD_SEX</th>\n",
" <th>GESTATIONAL_AGE</th>\n",
" <th>BMI</th>\n",
" <th>HEALTH</th>\n",
" <th>MALE</th>\n",
" <th>dBMI</th>\n",
" <th>PRETERM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9589</th>\n",
" <td>16.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>22.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-4.247242</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4943</th>\n",
" <td>17.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>24.8</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>-1.447242</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4604</th>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>37.8</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>11.552758</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13800</th>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>28.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1.752758</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11099</th>\n",
" <td>16.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>24.2</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>-2.047242</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VISIT_WT CHILD_SEX GESTATIONAL_AGE BMI HEALTH MALE dBMI \\\n",
"9589 16.0 2.0 4.0 22.0 1.0 0 -4.247242 \n",
"4943 17.0 2.0 4.0 24.8 2.0 0 -1.447242 \n",
"4604 16.0 1.0 4.0 37.8 2.0 1 11.552758 \n",
"13800 14.0 2.0 4.0 28.0 1.0 0 1.752758 \n",
"11099 16.0 2.0 4.0 24.2 1.0 0 -2.047242 \n",
"\n",
" PRETERM \n",
"9589 0 \n",
"4943 0 \n",
"4604 0 \n",
"13800 0 \n",
"11099 0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset = data_merged[['VISIT_WT', 'CHILD_SEX', 'GESTATIONAL_AGE', 'BMI', 'HEALTH']].dropna()\n",
"analysis_subset['MALE'] = (analysis_subset.CHILD_SEX==1).astype(int)\n",
"analysis_subset['dBMI'] = analysis_subset.BMI - analysis_subset.BMI.mean()\n",
"analysis_subset['PRETERM'] = (analysis_subset.GESTATIONAL_AGE<4).astype(int)\n",
"analysis_subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"analysis_subset.VISIT_WT.hist();"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (2 chains in 2 jobs)\n",
"NUTS: [sd, HEALTH, dBMI, GESTATIONAL_AGE, MALE, Intercept]\n",
"Sampling 2 chains: 100%|██████████| 6000/6000 [00:40<00:00, 149.17draws/s]\n"
]
}
],
"source": [
"import pymc3 as pm\n",
"GLM = pm.glm.GLM\n",
"\n",
"model_formula = 'VISIT_WT ~ MALE + GESTATIONAL_AGE + dBMI + HEALTH'\n",
"\n",
"with pm.Model() as weight_model:\n",
" \n",
" lm = GLM.from_formula(model_formula, data=analysis_subset)\n",
" samples = pm.sample(1000, tune=2000, njobs=2)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pm.forestplot(samples, varnames=['MALE', 'GESTATIONAL_AGE', 'dBMI', 'HEALTH']);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"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></th>\n",
" <th>mean</th>\n",
" <th>sd</th>\n",
" <th>mc_error</th>\n",
" <th>hpd_2.5</th>\n",
" <th>hpd_97.5</th>\n",
" <th>n_eff</th>\n",
" <th>Rhat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Intercept</th>\n",
" <td>11.84</td>\n",
" <td>0.54</td>\n",
" <td>0.02</td>\n",
" <td>10.78</td>\n",
" <td>12.88</td>\n",
" <td>1015.20</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALE</th>\n",
" <td>1.51</td>\n",
" <td>0.11</td>\n",
" <td>0.00</td>\n",
" <td>1.31</td>\n",
" <td>1.75</td>\n",
" <td>1732.75</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GESTATIONAL_AGE</th>\n",
" <td>0.78</td>\n",
" <td>0.13</td>\n",
" <td>0.00</td>\n",
" <td>0.53</td>\n",
" <td>1.02</td>\n",
" <td>1049.85</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dBMI</th>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>-0.01</td>\n",
" <td>0.03</td>\n",
" <td>1545.33</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HEALTH</th>\n",
" <td>0.13</td>\n",
" <td>0.11</td>\n",
" <td>0.00</td>\n",
" <td>-0.08</td>\n",
" <td>0.34</td>\n",
" <td>1574.31</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sd</th>\n",
" <td>2.34</td>\n",
" <td>0.04</td>\n",
" <td>0.00</td>\n",
" <td>2.27</td>\n",
" <td>2.42</td>\n",
" <td>2055.47</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean sd mc_error hpd_2.5 hpd_97.5 n_eff Rhat\n",
"Intercept 11.84 0.54 0.02 10.78 12.88 1015.20 1.0\n",
"MALE 1.51 0.11 0.00 1.31 1.75 1732.75 1.0\n",
"GESTATIONAL_AGE 0.78 0.13 0.00 0.53 1.02 1049.85 1.0\n",
"dBMI 0.01 0.01 0.00 -0.01 0.03 1545.33 1.0\n",
"HEALTH 0.13 0.11 0.00 -0.08 0.34 1574.31 1.0\n",
"sd 2.34 0.04 0.00 2.27 2.42 2055.47 1.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pm.summary(samples).round(2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment