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{
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pymc3 as pm\n",
"import xarray as xr\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 2 jobs)\n",
"NUTS: [school_effect_raw, sd_schools_log__, mu_schools]\n",
"100%|██████████| 1500/1500 [00:03<00:00, 389.52it/s]\n"
]
}
],
"source": [
"# Data of the Eight Schools Model\n",
"schools = ['Tübingen', 'Markdorf', 'Friedrichshafen',\n",
" 'Dingelsdorf', 'Konstanz', 'Unteruhldingen',\n",
" 'Radolfzell', 'Meersburg']\n",
"y = np.array([28., 8., -3., 7., -1., 1., 18., 12.])\n",
"sigma = np.array([15., 10., 16., 11., 9., 11., 10., 18.])\n",
"\n",
"with pm.Model() as model:\n",
" mu = pm.Normal('mu_schools', mu=0, sd=5)\n",
" sd = pm.HalfNormal('sd_schools', sd=2)\n",
" raw = pm.Normal('school_effect_raw', shape=len(schools))\n",
" school_effect = mu + sd * raw\n",
" pm.Deterministic('school_effect', school_effect)\n",
" pm.Normal('obs', mu=school_effect, sd=sigma, observed=y)\n",
" trace = pm.sample(draws=1000, chains=4)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def to_xarray(trace, coords, dims):\n",
" \"\"\"Convert a pymc3 trace to an xarray dataset.\n",
"\n",
" Parameters\n",
" ----------\n",
" trace : pymc3 trace\n",
" coords : dict\n",
" A dictionary containing the values that are used as index. The key\n",
" is the name of the dimension, the values are the index values.\n",
" dims : dict[str, Tuple(str)]\n",
" A mapping from pymc3 variables to a tuple corresponding to\n",
" the shape of the variable, where the elements of the tuples are\n",
" the names of the coordinate dimensions.\n",
"\n",
" Example\n",
" -------\n",
" ::\n",
"\n",
" coords = {\n",
" 'subject': ['Peter', 'Hans'],\n",
" 'time': [Timestamp('2017-01-20'), Timestamp('2017-01-21')],\n",
" 'treatment': ['sorafenib', 'whatever']\n",
" }\n",
" dims = {\n",
" 'subject_mu': ('subject',),\n",
" 'effect': ('treatment',),\n",
" 'interaction': ('time', 'treatment'),\n",
" }\n",
" \"\"\"\n",
" coords = coords.copy()\n",
" coords['sample'] = list(range(len(trace)))\n",
" coords['chain'] = list(range(trace.nchains))\n",
" \n",
" coords_ = {}\n",
" for key, vals in coords.items():\n",
" coords_[key] = xr.IndexVariable((key,), data=vals)\n",
" coords = coords_\n",
" \n",
" data = xr.Dataset(coords=coords)\n",
" for key in trace.varnames:\n",
" if key.endswith('_'):\n",
" continue\n",
" dims_str = ('chain', 'sample')\n",
" if key in dims:\n",
" dims_str = dims_str + dims[key]\n",
" vals = trace.get_values(key, combine=False, squeeze=False)\n",
" vals = np.array(vals)\n",
" data[key] = xr.DataArray(vals, {v: coords[v] for v in dims_str}, dims=dims_str)\n",
" \n",
" return data\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"coords = {'school': schools}\n",
"dims = {\n",
" 'school_effect_raw': ('school',),\n",
" 'school_effect': ('school',),\n",
"}\n",
"\n",
"trace = to_xarray(trace, coords=coords, dims=dims)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (chain: 4, sample: 1000, school: 8)\n",
"Coordinates:\n",
" * school (school) <U15 'Tübingen' 'Markdorf' 'Friedrichshafen' ...\n",
" * sample (sample) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...\n",
" * chain (chain) int64 0 1 2 3\n",
"Data variables:\n",
" mu_schools (chain, sample) float64 4.444 4.697 8.5 4.816 1.655 ...\n",
" school_effect_raw (chain, sample, school) float64 0.2249 -0.8341 0.3967 ...\n",
" sd_schools (chain, sample) float64 1.09 1.163 0.126 0.4046 ...\n",
" school_effect (chain, sample, school) float64 4.689 3.534 4.876 ..."
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (chain: 4, school: 8)\n",
"Coordinates:\n",
" * school (school) <U15 'Tübingen' 'Markdorf' 'Friedrichshafen' ...\n",
" sample int64 10\n",
" * chain (chain) int64 0 1 2 3\n",
"Data variables:\n",
" mu_schools (chain) float64 5.043 8.441 7.48 8.69\n",
" school_effect_raw (chain, school) float64 0.05097 -0.9756 0.9208 ...\n",
" sd_schools (chain) float64 1.111 0.8415 0.04432 1.689\n",
" school_effect (chain, school) float64 5.1 3.959 6.066 4.008 4.194 ..."
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We get one draw per chain if we select a sample\n",
"trace.isel(sample=10)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# We can stack the ('chain', 'sample') dimension into one hierarchical index"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"trace_stacked = trace.stack(draw=('chain', 'sample'))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (draw: 4000, school: 8)\n",
"Coordinates:\n",
" * school (school) <U15 'Tübingen' 'Markdorf' 'Friedrichshafen' ...\n",
" * draw (draw) MultiIndex\n",
" - chain (draw) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n",
" - sample (draw) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ...\n",
"Data variables:\n",
" mu_schools (draw) float64 4.444 4.697 8.5 4.816 1.655 10.14 ...\n",
" school_effect_raw (school, draw) float64 0.2249 0.3931 -0.6127 -0.3396 ...\n",
" sd_schools (draw) float64 1.09 1.163 0.126 0.4046 0.2088 0.5627 ...\n",
" school_effect (school, draw) float64 4.689 5.155 8.422 4.679 1.188 ..."
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace_stacked"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
" * school (school) <U15 'Tübingen' 'Markdorf' 'Friedrichshafen' ...\n",
" draw object (0, 10)\n",
"Data variables:\n",
" mu_schools float64 5.043\n",
" school_effect_raw (school) float64 0.05097 -0.9756 0.9208 -0.9321 ...\n",
" sd_schools float64 1.111\n",
" school_effect (school) float64 5.1 3.959 6.066 4.008 4.194 4.442 ..."
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace_stacked.isel(draw=10)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (chain: 4, sample: 1000, school: 8)\n",
"Coordinates:\n",
" * school (school) <U15 'Tübingen' 'Markdorf' 'Friedrichshafen' ...\n",
" * chain (chain) int64 0 1 2 3\n",
" * sample (sample) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...\n",
"Data variables:\n",
" mu_schools (chain, sample) float64 4.444 4.697 8.5 4.816 1.655 ...\n",
" school_effect_raw (school, chain, sample) float64 0.2249 0.3931 -0.6127 ...\n",
" sd_schools (chain, sample) float64 1.09 1.163 0.126 0.4046 ...\n",
" school_effect (school, chain, sample) float64 4.689 5.155 8.422 ..."
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can recover the original array\n",
"trace_stacked.unstack('draw')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"# The hierarchical index can be a bit of an issue while plotting"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = trace.sd_schools.plot.line(hue='chain')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Plotting requires coordinates to be numeric or dates of type np.datetime64 or datetime.datetime.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-51-eb2c0218e59f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrace_stacked\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msd_schools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(darray, *args, **kwargs)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0myplt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0m_ensure_plottable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxplt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0mprimitive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36m_ensure_plottable\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 54\u001b[0m if not (_valid_numpy_subdtype(np.array(x), numpy_types) or\n\u001b[1;32m 55\u001b[0m _valid_other_type(np.array(x), other_types)):\n\u001b[0;32m---> 56\u001b[0;31m raise TypeError('Plotting requires coordinates to be numeric '\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0;34m'or dates of type np.datetime64 or '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m 'datetime.datetime.')\n",
"\u001b[0;31mTypeError\u001b[0m: Plotting requires coordinates to be numeric or dates of type np.datetime64 or datetime.datetime."
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = trace_stacked.sd_schools.plot.line()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11c573978>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# But pandas can do this, so this should be fixable in xarray\n",
"trace_stacked.sd_schools.to_pandas().plot()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x117ed5128>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# We can use seaborn for plotting\n",
"# It would be nice if we could get rid of that data mangling though.\n",
"_data = trace.school_effect.to_dataframe().reset_index()\n",
"sns.factorplot(x='school_effect', y='school', data=_data, kind='violin')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = trace.sd_schools.plot.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare models"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 2 jobs)\n",
"NUTS: [school_effect_raw, sd_schools_log__, mu_schools]\n",
"100%|██████████| 1500/1500 [00:03<00:00, 495.44it/s]\n"
]
}
],
"source": [
"# Data of the Eight Schools Model\n",
"schools = ['Tübingen', 'Markdorf', 'Friedrichshafen',\n",
" 'Dingelsdorf', 'Konstanz', 'Unteruhldingen',\n",
" 'Radolfzell', 'Meersburg']\n",
"y = np.array([28., 8., -3., 7., -1., 1., 18., 12.])\n",
"sigma = np.array([15., 10., 16., 11., 9., 11., 10., 18.])\n",
"\n",
"with pm.Model() as model2:\n",
" mu = pm.Normal('mu_schools', mu=0, sd=5)\n",
" sd = pm.HalfCauchy('sd_schools', beta=3) # We change sd!!\n",
" raw = pm.Normal('school_effect_raw', shape=len(schools))\n",
" school_effect = mu + sd * raw\n",
" pm.Deterministic('school_effect', school_effect)\n",
" pm.Normal('obs', mu=school_effect, sd=sigma, observed=y)\n",
" trace2 = pm.sample(draws=1000, chains=4, nuts_kwargs=dict(target_accept=0.9))"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"coords = {'school': schools}\n",
"dims = {\n",
" 'school_effect_raw': ('school',),\n",
" 'school_effect': ('school',),\n",
"}\n",
"\n",
"trace2 = to_xarray(trace2, coords=coords, dims=dims)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"trace['model'] = 'half-normal-sd'\n",
"trace2['model'] = 'half-cauchy-sd'"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"trace_all = xr.concat([trace, trace2], dim='model')"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (chain: 4, model: 2, sample: 1000, school: 8)\n",
"Coordinates:\n",
" * school (school) <U15 'Tübingen' 'Markdorf' 'Friedrichshafen' ...\n",
" * sample (sample) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...\n",
" * chain (chain) int64 0 1 2 3\n",
" * model (model) <U14 'half-normal-sd' 'half-cauchy-sd'\n",
"Data variables:\n",
" mu_schools (model, chain, sample) float64 4.444 4.697 8.5 4.816 ...\n",
" school_effect_raw (model, chain, sample, school) float64 0.2249 -0.8341 ...\n",
" sd_schools (model, chain, sample) float64 1.09 1.163 0.126 ...\n",
" school_effect (model, chain, sample, school) float64 4.689 3.534 ..."
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace_all"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11c9f0240>"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 685.625x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# We can use seaborn for plotting\n",
"_data = trace_all.school_effect.to_dataframe().reset_index()\n",
"sns.factorplot(x='school_effect', y='school', hue='model',\n",
" data=_data, kind='violin', size=8)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"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>model</th>\n",
" <th>chain</th>\n",
" <th>sample</th>\n",
" <th>school</th>\n",
" <th>school_effect</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>half-normal-sd</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Tübingen</td>\n",
" <td>4.688937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>half-normal-sd</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Markdorf</td>\n",
" <td>3.534422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>half-normal-sd</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Friedrichshafen</td>\n",
" <td>4.876193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>half-normal-sd</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Dingelsdorf</td>\n",
" <td>5.266836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>half-normal-sd</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Konstanz</td>\n",
" <td>4.552894</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model chain sample school school_effect\n",
"0 half-normal-sd 0 0 Tübingen 4.688937\n",
"1 half-normal-sd 0 0 Markdorf 3.534422\n",
"2 half-normal-sd 0 0 Friedrichshafen 4.876193\n",
"3 half-normal-sd 0 0 Dingelsdorf 5.266836\n",
"4 half-normal-sd 0 0 Konstanz 4.552894"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_data.head()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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