From 6e1d1c0ff645e3cde990187d26fc25d9742e17e1 Mon Sep 17 00:00:00 2001
From: thequackdaddy <pquack@gmail.com>
Date: Sun, 25 Feb 2018 18:57:43 -0600
Subject: [PATCH] Notebook shoudl probably use Gaussian distribution for GLM
 fit

---
 .../notebooks/distributed_estimation.ipynb    | 43 ++++++++-----------
 1 file changed, 17 insertions(+), 26 deletions(-)

diff --git a/examples/notebooks/distributed_estimation.ipynb b/examples/notebooks/distributed_estimation.ipynb
index 0a60f1f011c..89f1dc61ffd 100644
--- a/examples/notebooks/distributed_estimation.ipynb
+++ b/examples/notebooks/distributed_estimation.ipynb
@@ -9,13 +9,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "from scipy.stats.distributions import norm\n",
     "from statsmodels.base.distributed_estimation import DistributedModel\n",
     "\n",
     "def _exog_gen(exog, partitions):\n",
@@ -52,16 +51,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X = np.random.normal(size=(1000, 25))\n",
     "beta = np.random.normal(size=25)\n",
     "beta *= np.random.randint(0, 2, size=25)\n",
-    "y = X.dot(beta) + np.random.normal(size=1000)\n",
+    "y = norm.rvs(loc=X.dot(beta))\n",
     "m = 5"
    ]
   },
@@ -74,10 +71,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "debiased_OLS_mod = DistributedModel(m)\n",
@@ -94,17 +89,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "scrolled": false
    },
    "outputs": [],
    "source": [
     "from statsmodels.genmod.generalized_linear_model import GLM\n",
-    "from statsmodels.genmod.families import Binomial\n",
+    "from statsmodels.genmod.families import Gaussian\n",
     "\n",
     "debiased_GLM_mod = DistributedModel(m, model_class=GLM,\n",
-    "                                    init_kwds={\"family\": Binomial()})\n",
+    "                                    init_kwds={\"family\": Gaussian()})\n",
     "debiased_GLM_fit = debiased_GLM_mod.fit(zip(_endog_gen(y, m), _exog_gen(X, m)),\n",
     "                                        fit_kwds={\"alpha\": 0.2})"
    ]
@@ -118,10 +113,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from statsmodels.base.distributed_estimation import _est_regularized_naive, _join_naive\n",
@@ -142,10 +135,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from statsmodels.base.distributed_estimation import _est_unregularized_naive, DistributedResults\n",
@@ -175,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.14"
   }
  },
  "nbformat": 4,