--- a
+++ b/4x/Data_analysis.ipynb
@@ -0,0 +1,1905 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:9cd3ab0afcb29eac6d6bd69a7801fa0762bc2b9f4f4135e6340765e9e0ce06ef"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "from matplotlib import rcParams\n",
+      "import matplotlib.pyplot as plt\n",
+      "import seaborn\n",
+      "import pandas as pd\n",
+      "import itertools\n",
+      "import os\n",
+      "from sklearn import linear_model, ensemble, decomposition, cross_validation, preprocessing\n",
+      "from statsmodels.regression.mixed_linear_model import MixedLM\n",
+      "import statsmodels\n",
+      "import statsmodels.api as sm\n",
+      "from statsmodels.regression.linear_model import OLSResults\n",
+      "from statsmodels.tools.tools import add_constant\n",
+      "from sklearn.neighbors import KernelDensity\n",
+      "\n",
+      "from mpl_toolkits.mplot3d import Axes3D, proj3d\n",
+      "\n",
+      "print statsmodels.__version__\n",
+      "\n",
+      "%matplotlib inline\n",
+      "rcParams[\"figure.figsize\"] = (14, 8)\n",
+      "\n",
+      "rcParams[\"text.usetex\"] = False\n",
+      "rcParams[\"xtick.labelsize\"] = 12\n",
+      "rcParams[\"ytick.labelsize\"] = 12\n",
+      "rcParams[\"font.size\"] = 14\n",
+      "rcParams[\"axes.titlesize\"] = 16\n",
+      "#rcParams[\"text.usetex\"] = False\n",
+      "rcParams[\"font.family\"] = \"Serif\"\n",
+      "rcParams[\"figure.dpi\"] = 600"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.7.0.dev-c8e980d\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# RAW DATA\n",
+      "\n",
+      "raw_physical = pd.read_csv(\"../data/physical.csv\")\n",
+      "raw_histo = pd.read_csv(\"../data/tawfik.csv\")\n",
+      "ent = pd.read_csv(\"results/entropy.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "foci = pd.read_csv(\"results/foci.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "lac = pd.read_csv(\"results/normalised_lacunarity.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "gabor = pd.read_csv(\"results/gabor_filters.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "ts = pd.read_csv(\"results/tissue_sinusoid_ratio.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "blur = pd.read_csv(\"results/blur.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "distances = pd.read_csv(\"results/interfoci_dist.csv\").drop([\"Unnamed: 0\"], 1)\n",
+      "\n",
+      "raw_image = pd.merge(lac, ent,\n",
+      "\ton=[\"Sheep\", \"Image\"]).merge(foci, \n",
+      "\ton=[\"Sheep\", \"Image\"]).merge(gabor,\n",
+      "\ton=[\"Sheep\", \"Image\"]).merge(ts, \n",
+      "\ton=[\"Sheep\", \"Image\"]).merge(blur, \n",
+      "\ton=[\"Sheep\", \"Image\"]).merge(distances, \n",
+      "    on=[\"Sheep\", \"Image\"])\n",
+      "raw_image.rename(columns = {\t\"meanSize\" : \"FociSize\", \n",
+      "\t\t\t\t\t\t\t\t\"TSRatio\" : \"TissueToSinusoid\",\n",
+      "\t\t\t\t\t\t\t\t\"Count\" : \"FociCount\" }, inplace=True)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def normalise(df, skip = []) :\n",
+      "\tfor i in df.columns :\n",
+      "\t\tif i not in skip :\n",
+      "\t\t\tdf[i] -= df[i].mean()\n",
+      "\t\t\tdf[i] /= df[i].std()\n",
+      "\treturn df\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "def rescale(df, skip = []) :\n",
+      "    for i in df.columns :\n",
+      "        if i not in skip :\n",
+      "            df[i] -= df[i].min()\n",
+      "            df[i] /= df[i].max()\n",
+      "    return df\n",
+      "\n",
+      "\n",
+      "\n",
+      "# Remove a layer from a list\n",
+      "def delayer(m) :\n",
+      "\tout = []\n",
+      "\tfor i in m :\n",
+      "\t\tif isinstance(i, list) :\n",
+      "\t\t\tfor j in i :\n",
+      "\t\t\t\tout.append(j)\n",
+      "\t\telse :\n",
+      "\t\t\tout.append(i)\n",
+      "\treturn out\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "# Remove all layers from a list\n",
+      "def flatten(m) :\n",
+      "\tout = m[:]\n",
+      "\n",
+      "\twhile out != delayer(out) :\n",
+      "\t\tout = delayer(out)\n",
+      "\n",
+      "\treturn out\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "# Generate all combinations of objects in a list\n",
+      "def combinatorial(l, short = np.inf) :\n",
+      "\tout = []\n",
+      "\n",
+      "\tfor numel in range(len(l)) :\n",
+      "\t\tfor i in itertools.combinations(l, numel+1) :\n",
+      "\t\t\tif len(list(i)) < short :\n",
+      "\t\t\t\tout.append(list(i))\n",
+      "\n",
+      "\treturn out\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "def pcaplot(df) :\n",
+      "\n",
+      "\t# PCA\n",
+      "\tpca = decomposition.PCA(whiten = True)\n",
+      "\tpca.fit(df)\n",
+      "\tp1 = pca.components_[0] / np.abs(pca.components_[0]).max() * np.sqrt(2)/2\n",
+      "\tp2 = pca.components_[1] / np.abs(pca.components_[1]).max() * np.sqrt(2)/2\n",
+      "\n",
+      "\t# Normalise\n",
+      "\tnorms = np.max([np.sqrt((np.array(zip(p1, p2)[i])**2).sum()) for i in range(len(p1))])\n",
+      "\tc = plt.Circle( (0, 0), radius = 1, alpha = 0.2)\n",
+      "\tplt.axes(aspect = 1)\n",
+      "\tplt.gca().add_artist(c)\n",
+      "\n",
+      "\tplt.scatter(p1 / norms, p2 / norms)\n",
+      "\tplt.xlim([-1, 1])\n",
+      "\tplt.ylim([-1, 1])\n",
+      "\n",
+      "\tfor i, text in enumerate(df.columns) :\n",
+      "\t\tplt.annotate(text, xy = [p1[i], p2[i]])\n",
+      "\n",
+      "\tplt.tight_layout()\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "def big_ass_matrix(df, y, x, group = None, short = True) :\n",
+      "\n",
+      "    independent = combinatorial(x, short)\n",
+      "    \n",
+      "    models = {}\n",
+      "    p = {}\n",
+      "    aic = {}\n",
+      "    r2 = {}\n",
+      "    best = {}\n",
+      "    dfs = {}\n",
+      "    bestdf = {}\n",
+      "    \n",
+      "    for dependent in y :\n",
+      "        \n",
+      "        print \"Regressing for %s\" % dependent\n",
+      "        \n",
+      "        for covariate in independent :\n",
+      "            \n",
+      "            if group is None :\n",
+      "                \n",
+      "                subset = delayer([covariate, dependent])\n",
+      "                df2 = df[subset].dropna()\n",
+      "                df2[\"Intercept\"] = np.ones(len(df2))\n",
+      "                dfs.setdefault(dependent, []).append(df2)\n",
+      "                \n",
+      "                ols = sm.GLS(endog=df2[dependent], exog=df2[delayer([covariate, \"Intercept\"])]).fit()\n",
+      "                \n",
+      "                models.setdefault(dependent, []).append(ols)\n",
+      "                p.setdefault(dependent, []).append(ols.pvalues[:-1].values)\n",
+      "                aic.setdefault(dependent, []).append(ols.aic)\n",
+      "                r2.setdefault(dependent, []).append(ols.rsquared)\n",
+      "            \n",
+      "            else :\n",
+      "                \n",
+      "                subset = delayer([covariate, dependent, group])\n",
+      "                df2 = df[subset].dropna()\n",
+      "                dfs.setdefault(dependent, []).append(df2)\n",
+      "                \n",
+      "                ols = MixedLM.from_formula(rstr(y=dependent, x=covariate), data=df2, groups=df2[group]).fit()\n",
+      "                \n",
+      "                models.setdefault(dependent, []).append(ols)\n",
+      "                aic.setdefault(dependent, []).append(2 * (ols.k_fe + 1) - 2 * ols.llf)\n",
+      "                p.setdefault(dependent, []).append(ols.pvalues[1:-1].values)\n",
+      "                r2.setdefault(dependent, []).append(mmR2(df2, ols))\n",
+      "\n",
+      "    \n",
+      "       \n",
+      "        bestAIC = np.min(aic[dependent])\n",
+      "        \n",
+      "        for i, val in enumerate(models[dependent]) :\n",
+      "            \n",
+      "            if aic[dependent][i] < 2 + bestAIC :\n",
+      "                \n",
+      "                if np.sum(p[dependent][i] > 0.05) == 0 :\n",
+      "                    \n",
+      "                    if group is None :\n",
+      "                        \n",
+      "                        best.setdefault(dependent, []).append(val)\n",
+      "                        bestdf.setdefault(dependent, []).append(dfs[dependent][i])\n",
+      "                        \n",
+      "                    else :\n",
+      "                        \n",
+      "                        if val.random_effects.abs().mean()[0] > 0.01 :\n",
+      "                            \n",
+      "                            best.setdefault(dependent, []).append(val)\n",
+      "                            bestdf.setdefault(dependent, []).append(dfs[dependent][i])\n",
+      "       \n",
+      "            \n",
+      "        if best.has_key(dependent) :\n",
+      "            for i, model in enumerate(best[dependent]) :\n",
+      "                \n",
+      "                if not os.path.exists(\"regressions/%s\" % dependent) :                  \n",
+      "                    os.mkdir(\"regressions/%s\" % dependent)\n",
+      "                    \n",
+      "                if not os.path.exists(\"../talk/figures/regressions/%s\" % dependent) :                  \n",
+      "                    os.mkdir(\"../talk/figures/regressions/%s\" % dependent)                \n",
+      "\n",
+      "                if group is None :\n",
+      "\n",
+      "                    dfx = bestdf[dependent][i]\n",
+      "                    plt.scatter(model.fittedvalues.values, dfx[model.model.endog_names].values, c=seaborn.color_palette(\"deep\", 8)[0])\n",
+      "                    plt.plot(dfx[model.model.endog_names].values, dfx[model.model.endog_names].values, c=seaborn.color_palette(\"deep\", 8)[2])\n",
+      "                    plt.ylabel(model.model.endog_names)\n",
+      "                    yl = model.model.exog_names[:]\n",
+      "                    yl.remove(\"Intercept\")\n",
+      "                    plt.xlabel(\"Estimate using \" + \", \".join(yl))\n",
+      "                    plt.title(rstr(dependent, model.model.exog_names).replace(\" + Intercept\", \"\"))\n",
+      "                    #plt.title(r\"$R^2$ = %.02f\" % model.rsquared)\n",
+      "                    st = (\"$R^2$ = %.03f\\n\\n\"% model.rsquared)\n",
+      "                    for coefnum, coef in enumerate(yl) :\n",
+      "                        st += (\"%s\" % coef)\n",
+      "                        st += (\" : %.03f\\n\" % model.params[coef])\n",
+      "                        st += (\"$p$ = %.01e\\n\\n\" % model.pvalues[coefnum])\n",
+      "                    #plt.suptitle(st)\n",
+      "                    plt.text(0.01, .99, st, va=\"top\", ha=\"left\")\n",
+      "                    plt.xlim([-0.05, 1.05])\n",
+      "                    plt.ylim([-0.05, 1.05])\n",
+      "                    plt.savefig(\"regressions/%s/lm-%d.pdf\" % (dependent, i))\n",
+      "                    plt.savefig(\"../talk/figures/regressions/%s/lm-%d.png\" % (dependent, i), dpi=300, jpeg_quality=90)\n",
+      "                    plt.close()\n",
+      "\n",
+      "                else :\n",
+      "                    dfx = bestdf[dependent][i]\n",
+      "                    y, yhat = mmPredict(model.model.data.frame, model)\n",
+      "                    plt.scatter(yhat, y, c=seaborn.color_palette(\"deep\", 8)[0])\n",
+      "                    plt.plot(y, y, c=seaborn.color_palette(\"deep\", 8)[2])\n",
+      "                    plt.ylabel(model.model.endog_names)\n",
+      "                    yl = model.model.exog_names[:]\n",
+      "                    yl.remove(\"Intercept\")\n",
+      "                    plt.xlabel(\"Estimate using \" + \", \".join(yl))\n",
+      "                    plt.title(rstr(dependent, model.model.exog_names).replace(\"Intercept + \", \"\"))\n",
+      "                    \n",
+      "                    #plt.title(r\"$R^2$ = %.02f\" % mmR2(dfx, model))\n",
+      "                    st = (\"$R^2$ = %.03f\\n\\n\" % mmR2(dfx, model))\n",
+      "                    for coefnum, coef in enumerate(yl) :\n",
+      "                        st += coef\n",
+      "                        st += \" : %.03f\\n\" % model.fe_params[1+coefnum]\n",
+      "                        st += \"$p$ = %.01e\\n\\n\" % model.pvalues[coef]\n",
+      "                    st += (\"Avg. abs. RE coef. : %.03f\" % model.random_effects.abs().mean())\n",
+      "                    plt.text(0.01, .99, st, va=\"top\", ha=\"left\")\n",
+      "                    \n",
+      "                    plt.xlim([-0.05, 1.05])\n",
+      "                    plt.ylim([-0.05, 1.05])\n",
+      "                    plt.savefig(\"regressions/%s/mm_%d.pdf\" % (dependent, i))\n",
+      "                    plt.savefig(\"../talk/figures/regressions/%s/mm_%d.png\" % (dependent, i), dpi=300, jpeg_quality=90)\n",
+      "                    plt.close()\n",
+      "        \n",
+      "    return best, (models, p, r2, aic)\n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "def summary(models) :\n",
+      "\n",
+      "\t# Generate list of everything\n",
+      "\tr2 = np.array([m.r2 for dependent in models.keys() for m in models[dependent]])\n",
+      "\tp = np.array([m.f_stat[\"p-value\"] for dependent in models.keys() for m in models[dependent]])\n",
+      "\tmod = np.array([m for dependent in models.keys() for m in models[dependent]])\n",
+      "\tdependent = np.array([dependent for dependent in models.keys() for m in models[dependent]])\n",
+      "\n",
+      "\t# Sort by R2\n",
+      "\tidx = np.argsort(r2)[::-1]\n",
+      "\n",
+      "\t# Output string\n",
+      "\ts = \"%d significant regressions.\\n\\n\" % len(r2)\n",
+      "\ts += \"Ten most correlated :\\n\\n\"\n",
+      "\n",
+      "\t# Print a summary of the top ten correlations\n",
+      "\tfor i in idx[:10] :\n",
+      "\t\ts += (\"%s ~ %s\\n\" % (dependent[i], \" + \".join(mod[i].x.columns[:-1])))\n",
+      "\t\ts += (\"R^2 = %f\\tp = %f\\n\\n\" % (r2[i], p[i]))\n",
+      "\n",
+      "\tprint s\n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "    \n",
+      "def rstr(y, x) :\n",
+      "    formatstr = \"%s ~ \" % y\n",
+      "    for i in x[:-1] :\n",
+      "        formatstr += str(i)\n",
+      "        formatstr += \" + \"\n",
+      "    formatstr += str(x[-1])\n",
+      "    return formatstr\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "def mmR2(df, ols) :\n",
+      "\n",
+      "    y = df[ols.model.endog_names]\n",
+      "    sigma_a = ols.random_effects.values.var()\n",
+      "\n",
+      "    yhat = np.zeros_like(y)\n",
+      "    for i, coef in enumerate(ols.fe_params) :\n",
+      "        yhat += ols.model.data.exog[:, i] * coef\n",
+      "\n",
+      "    sigma_f = yhat.var()\n",
+      "\n",
+      "    idxx = []\n",
+      "    \n",
+      "    grouplabel = list(df.columns)\n",
+      "    grouplabel.remove(ols.model.endog_names)\n",
+      "    remove_these = ols.model.exog_names[:]\n",
+      "    remove_these.remove(\"Intercept\")\n",
+      "    for i in remove_these :\n",
+      "        grouplabel.remove(i)\n",
+      "\n",
+      "    for j, u in enumerate(ols.model.group_labels) :\n",
+      "        idx = np.where(df[grouplabel] == u)[0]\n",
+      "        yhat[idx] += ols.random_effects.values[j]\n",
+      "\n",
+      "    sigma_e = np.var(yhat - y)\n",
+      "\n",
+      "    return (sigma_f + sigma_a) / (sigma_f + sigma_a + sigma_e)\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "def mmPredict(df, ols) :\n",
+      "\n",
+      "    y = df[ols.model.endog_names]\n",
+      "    sigma_a = ols.random_effects.values.var()\n",
+      "\n",
+      "    yhat = np.zeros_like(y)\n",
+      "    for i, coef in enumerate(ols.fe_params) :\n",
+      "        yhat += ols.model.data.exog[:, i] * coef\n",
+      "\n",
+      "    sigma_f = yhat.var()\n",
+      "\n",
+      "    idxx = []\n",
+      "    \n",
+      "    grouplabel = list(df.columns)\n",
+      "    grouplabel.remove(ols.model.endog_names)\n",
+      "    remove_these = ols.model.exog_names[:]\n",
+      "    remove_these.remove(\"Intercept\")\n",
+      "    for i in remove_these :\n",
+      "        grouplabel.remove(i)\n",
+      "\n",
+      "    for j, u in enumerate(ols.model.group_labels) :\n",
+      "        idx = np.where(df[grouplabel] == u)[0]\n",
+      "        yhat[idx] += ols.random_effects.values[j]\n",
+      "        \n",
+      "    return y, yhat\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "#best, stuff = big_ass_matrix(df=sheep, y=[\"Weight\"], x=imagecols, group=\"AgeAtDeath\", short=True)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "raw_image.columns"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 58,
+       "text": [
+        "Index([u'Sheep', u'Image', u'Lacunarity', u'Entropy', u'FociCount', u'FociSize', u'stdSize', u'Scale', u'Directionality', u'TissueToSinusoid', u'Blur', u'MinDist', u'IFDist'], dtype='object')"
+       ]
+      }
+     ],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# CLEAN DATA\n",
+      "\n",
+      "physcols = [\"Weight\", \"Sex\", \"AgeAtDeath\", \"Foreleg\", \"Hindleg\", \"E\", \"CES\", \"RES\"]\n",
+      "imagecols = [\"Entropy\", \"Lacunarity\", \"Inflammation\", \"Scale\", \"Directionality\", \"FociCount\", \"FociSize\", \"TissueToSinusoid\", \"Blur\", \"MinDist\", \"IFDist\"]\n",
+      "histcols = [\"LobularCollapse\", \"InterfaceHepatitis\", \"ConfluentNecrosis\", \"LnApRi\", \"PortalInflammation\", \"BDHyperplasia\", \"Fibrosis\", \"TawfikTotal\", \"MeanHepSize\", \"MinHepSize\", \"MaxHepSize\"]\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "# IMAGE\n",
+      "\n",
+      "# Set FociSize to zero if FociCount is zero\n",
+      "# Drop stdSize\n",
+      "image = raw_image\n",
+      "image = image.drop(\"stdSize\", 1)\n",
+      "image.FociSize[raw_image.FociCount == 0] = 0\n",
+      "\n",
+      "\n",
+      "\n",
+      "# HISTO\n",
+      "\n",
+      "histo = raw_histo\n",
+      "histo = histo.drop([\"Vessels\", \"Vacuol\", \"Pigment\", \"Std_hep_size\"], 1)\n",
+      "\n",
+      "\n",
+      "\n",
+      "# PHYSICAL\n",
+      "physical = raw_physical\n",
+      "physical = physical.drop([\"CurrTag\", \"DeathDate\", \"Category\"], 1)\n",
+      "exposure = pd.read_csv(\"../data/exposure.csv\")\n",
+      "exposure.rename(columns={\"BirthYear\" : \"BirthYear\", \"AvgOfLambWS\" : \"E\"}, inplace=True)\n",
+      "exposure[\"AgeAtDeath\"] = 2011 - exposure.BirthYear\n",
+      "exposure.E = np.round(1. - exposure.E, 6)\n",
+      "exposure[\"CES\"] = np.flipud(np.flipud(exposure.E).cumsum())\n",
+      "exposure[\"RES\"] = np.flipud(np.flipud(exposure.E - exposure.CES / (exposure.AgeAtDeath + 1)).cumsum())\n",
+      "physical = pd.merge(physical, exposure, on=\"AgeAtDeath\", how=\"inner\")\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "# COMPLETE DATASET\n",
+      "\n",
+      "raw_data = pd.merge(pd.merge(image, histo, on=\"Sheep\", how=\"outer\"), physical, on=\"Sheep\", how=\"outer\")\n",
+      "raw_data.to_csv(\"../data/tentative_complete.csv\")\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "# AVERAGED BY SHEEP\n",
+      "data = raw_data\n",
+      "data[\"Inflammation\"] = data.FociCount * data.FociSize\n",
+      "\n",
+      "sheep = np.round(rescale(data.groupby(\"Sheep\").mean()), 9)\n",
+      "\n",
+      "\n",
+      "\n",
+      "sheep.rename(columns = {\"Lobular_collapse\" : \"LobularCollapse\", \n",
+      "                        \"Interface_hepatitis\" : \"InterfaceHepatitis\",\n",
+      "                        \"Confluent_necrosis\" : \"ConfluentNecrosis\",\n",
+      "                        \"Ln_ap_ri\" : \"LnApRi\",\n",
+      "                        \"Portal_inflammation\" : \"PortalInflammation\",\n",
+      "                        \"BD_hyperplasia\" : \"BDHyperplasia\",\n",
+      "                        \"Mean_hep_size\" : \"MeanHepSize\",\n",
+      "                        \"Min_hep_size\" : \"MinHepSize\",\n",
+      "                        \"Max_hep_size\" : \"MaxHepSize\"}, inplace=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "data.rename(columns = {\"Lobular_collapse\" : \"LobularCollapse\", \n",
+      "                        \"Interface_hepatitis\" : \"InterfaceHepatitis\",\n",
+      "                        \"Confluent_necrosis\" : \"ConfluentNecrosis\",\n",
+      "                        \"Ln_ap_ri\" : \"LnApRi\",\n",
+      "                        \"Portal_inflammation\" : \"PortalInflammation\",\n",
+      "                        \"BD_hyperplasia\" : \"BDHyperplasia\",\n",
+      "                        \"Mean_hep_size\" : \"MeanHepSize\",\n",
+      "                        \"Min_hep_size\" : \"MinHepSize\",\n",
+      "                        \"Max_hep_size\" : \"MaxHepSize\"}, inplace=True)\n",
+      "\n",
+      "#age = rescale(data.groupby(\"Sheep\").mean().dropna(subset=delayer([imagecols, histcols, \"AgeAtDeath\"])).groupby(\"AgeAtDeath\").mean())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "-c:17: SettingWithCopyWarning: \n",
+        "A value is trying to be set on a copy of a slice from a DataFrame\n",
+        "\n",
+        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pcols = physcols[:]\n",
+      "pcols.remove(\"AgeAtDeath\")\n",
+      "\n",
+      "print \" -------- 1\"\n",
+      "best_lm_phys, stuff_lm_phys = big_ass_matrix(df=sheep, y=physcols, x=imagecols, group=None, short=5)\n",
+      "\n",
+      "print \" -------- 2\"\n",
+      "best_lm_hist, stuff_lm_hist = big_ass_matrix(df=sheep, y=histcols, x=imagecols, group=None, short=5)\n",
+      "\n",
+      "print \" -------- 3\"\n",
+      "best_mm_phys, stuff_mm_phys = big_ass_matrix(df=sheep, y=pcols, x=imagecols, group=\"AgeAtDeath\", short=5)\n",
+      "\n",
+      "print \" -------- 4\"\n",
+      "best_mm_hist, stuff_mm_hist = big_ass_matrix(df=sheep, y=histcols, x=imagecols, group=\"AgeAtDeath\", short=5)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " -------- 1\n",
+        "Regressing for Weight\n",
+        "Regressing for Sex"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for AgeAtDeath"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for Foreleg"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for Hindleg"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for E"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for CES"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for RES"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        " -------- 2"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for LobularCollapse\n",
+        "Regressing for InterfaceHepatitis"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for ConfluentNecrosis"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for LnApRi"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for PortalInflammation"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for BDHyperplasia"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for Fibrosis"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for TawfikTotal"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Regressing for MeanHepSize"
+       ]
+      }
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y = \"BDHyperplasia\"\n",
+      "x = [\"Inflammation\", \"Scale\", \"Directionality\"]\n",
+      "\n",
+      "dfx = sheep[delayer([x, y, \"AgeAtDeath\"])].dropna()\n",
+      "model = MixedLM.from_formula(rstr(y, x), data=dfx, groups=\"AgeAtDeath\").fit()\n",
+      "#model = sm.GLS(endog=dfx.Portal_inflammation, exog=dfx[[\"FociSize\", \"AgeAtDeath\"]]).fit()\n",
+      "\n",
+      "dfx = sheep[[\"BDHyperplasia\", \"Inflammation\", \"AgeAtDeath\"]].dropna()\n",
+      "model2 = MixedLM.from_formula(rstr(y, [\"Inflammation\"]), data=dfx, groups=\"AgeAtDeath\").fit()\n",
+      "\n",
+      "dfx = sheep[[\"BDHyperplasia\", \"FociSize\", \"AgeAtDeath\"]].dropna()\n",
+      "model3 = MixedLM.from_formula(rstr(y, [\"FociSize\"]), data=dfx, groups=\"AgeAtDeath\").fit()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ss = \"E\"\n",
+      "s = np.array([sheep[sheep.AgeAtDeath == model.random_effects.index.values[i]][ss].iloc[0] for i in range(len(model.random_effects.index.values))])\n",
+      "s -= s.min()\n",
+      "s /= s.max()\n",
+      "\n",
+      "idx = np.round(s * 2000).astype(int)\n",
+      "C = seaborn.color_palette(\"coolwarm\", 2001)\n",
+      "CC = []\n",
+      "for i in idx :\n",
+      "    CC.append(C[i])\n",
+      "\n",
+      "\n",
+      "fig = plt.figure()\n",
+      "ax = fig.add_subplot(111, projection='3d')\n",
+      "plt.scatter(model.random_effects, model2.random_effects, zs=model3.random_effects, s=450, c=CC, alpha=1)#, c=np.array(C)[idx.astype(int)], alpha=1)\n",
+      "plt.title(\"Biliary Hyperplasia : Coefficients of Birth Year Random Effect\\nFirst component of PCA explains 98.3%% of variance.\\nWarmer colours - higher %s\" % ss, y=0.9)\n",
+      "\n",
+      "for i, sheep_age in enumerate(model.random_effects.index) :\n",
+      "    sheep_e = sheep[sheep.AgeAtDeath == sheep_age].E.iloc[0]\n",
+      "    x2, y2, _ = proj3d.proj_transform(model.random_effects.values[i], model2.random_effects.values[i], model3.random_effects.values[i], ax.get_proj())\n",
+      "    plt.annotate(int(np.round(2011 - sheep_age * 11)), xy=(x2, y2), xytext=(x2-0.0105, y2-0.002))\n",
+      "\n",
+      "ax.set_axis_bgcolor(\"white\")\n",
+      "plt.savefig(\"../talk/figures/regressions/BDHyperplasia/mm_coefs_color_%s.png\" % ss, dpi=600, jpeg_quality=100)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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r1w5TpkzB3r176SKDRCLBtm3bIJVK6c6Ht7c3tLS0lO77mJgYPH78WGlhqU2b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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x276d2c110>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(exposure.BirthYear, exposure.CES)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 550,
+       "text": [
+        "<matplotlib.collections.PathCollection at 0x151a71d90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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DAABUR+gAAADVEToAAEB1hA4AAFAdoQMAAFRH6AAAANUROgAAQHWEDgAAUB2hAwAAVEfo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\nDgAAUB2hAwAAVOf/AxGtze07aL60AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1157d6e10>"
+       ]
+      }
+     ],
+     "prompt_number": 550
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(model.random_effects.index.values, idx)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 460,
+       "text": [
+        "<matplotlib.collections.PathCollection at 0x14feaf790>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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aIYAAAIBmCCAAAKAZAggAAGjGJZM8uJRyIMntSS5L8kiSm2utJ8+w741J/rDW+pOTHBMA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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x152e7ad50>"
+       ]
+      }
+     ],
+     "prompt_number": 460
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\n",
+      "#plt.plot([sheep[np.round(sheep.AgeAtDeath * 11) == i].E.iloc[0] for i in range(11)])\n",
+      "#plt.plot(exposure.AgeAtDeath, exposure.E)\n",
+      "C2 = []\n",
+      "for i in (np.unique(sheep[[\"AgeAtDeath\", \"BD_hyperplasia\"]].dropna().AgeAtDeath) * 11).astype(int) :\n",
+      "    C2.append(sheep[np.round(sheep.AgeAtDeath * 11) == i].E.iloc[0])\n",
+      "#sheep[[\"AgeAtDeath\", \"E\"]].dropna()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 370
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.plot(exposure.BirthYear, exposure.E)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 401,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x14f3002d0>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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/EIlEsgD+ewAfiaI4BeDfAPj39Q6UWkuxVMGFK5OwWYw4e7xb6XC2zOuywmQU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\nrD3S3rjIbbSNfT9NUPJ6o8cPKz8AgBoQ/AA+1h0Jq6+7ozXT3kqrWRQ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+       "text": [
+        "<matplotlib.figure.Figure at 0x14df9c450>"
+       ]
+      }
+     ],
+     "prompt_number": 401
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "exposure"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>BirthYear</th>\n",
+        "      <th>E</th>\n",
+        "      <th>AgeAtDeath</th>\n",
+        "      <th>CES</th>\n",
+        "      <th>RES</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0 </th>\n",
+        "      <td> 1995</td>\n",
+        "      <td> 0.488722</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 11.143270</td>\n",
+        "      <td>-0.342905</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1 </th>\n",
+        "      <td> 1996</td>\n",
+        "      <td> 0.738562</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 10.654548</td>\n",
+        "      <td>-0.176141</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2 </th>\n",
+        "      <td> 1997</td>\n",
+        "      <td> 0.769231</td>\n",
+        "      <td> 14</td>\n",
+        "      <td>  9.915986</td>\n",
+        "      <td>-0.248793</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3 </th>\n",
+        "      <td> 1998</td>\n",
+        "      <td> 0.942408</td>\n",
+        "      <td> 13</td>\n",
+        "      <td>  9.146755</td>\n",
+        "      <td>-0.356959</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4 </th>\n",
+        "      <td> 1999</td>\n",
+        "      <td> 0.352000</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  8.204347</td>\n",
+        "      <td>-0.646027</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5 </th>\n",
+        "      <td> 2000</td>\n",
+        "      <td> 0.333333</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.852347</td>\n",
+        "      <td>-0.366923</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6 </th>\n",
+        "      <td> 2001</td>\n",
+        "      <td> 0.963415</td>\n",
+        "      <td> 10</td>\n",
+        "      <td>  7.519014</td>\n",
+        "      <td>-0.045894</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td> 2002</td>\n",
+        "      <td> 0.544218</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  6.555599</td>\n",
+        "      <td>-0.325762</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8 </th>\n",
+        "      <td> 2003</td>\n",
+        "      <td> 0.258065</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  6.011381</td>\n",
+        "      <td>-0.214420</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9 </th>\n",
+        "      <td> 2004</td>\n",
+        "      <td> 0.968627</td>\n",
+        "      <td>  7</td>\n",
+        "      <td>  5.753316</td>\n",
+        "      <td> 0.195446</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td> 2005</td>\n",
+        "      <td> 0.784946</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  4.784689</td>\n",
+        "      <td>-0.054017</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>11</th>\n",
+        "      <td> 2006</td>\n",
+        "      <td> 0.779874</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  3.999743</td>\n",
+        "      <td>-0.155436</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>12</th>\n",
+        "      <td> 2007</td>\n",
+        "      <td> 0.500000</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3.219869</td>\n",
+        "      <td>-0.268686</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td> 2008</td>\n",
+        "      <td> 0.608696</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  2.719869</td>\n",
+        "      <td>-0.124712</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td> 2009</td>\n",
+        "      <td> 0.639594</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  2.111173</td>\n",
+        "      <td>-0.053441</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>15</th>\n",
+        "      <td> 2010</td>\n",
+        "      <td> 0.746479</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  1.471579</td>\n",
+        "      <td> 0.010690</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>16</th>\n",
+        "      <td> 2011</td>\n",
+        "      <td> 0.725100</td>\n",
+        "      <td>  0</td>\n",
+        "      <td>  0.725100</td>\n",
+        "      <td> 0.000000</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 528,
+       "text": [
+        "    BirthYear         E  AgeAtDeath        CES       RES\n",
+        "0        1995  0.488722          16  11.143270 -0.342905\n",
+        "1        1996  0.738562          15  10.654548 -0.176141\n",
+        "2        1997  0.769231          14   9.915986 -0.248793\n",
+        "3        1998  0.942408          13   9.146755 -0.356959\n",
+        "4        1999  0.352000          12   8.204347 -0.646027\n",
+        "5        2000  0.333333          11   7.852347 -0.366923\n",
+        "6        2001  0.963415          10   7.519014 -0.045894\n",
+        "7        2002  0.544218           9   6.555599 -0.325762\n",
+        "8        2003  0.258065           8   6.011381 -0.214420\n",
+        "9        2004  0.968627           7   5.753316  0.195446\n",
+        "10       2005  0.784946           6   4.784689 -0.054017\n",
+        "11       2006  0.779874           5   3.999743 -0.155436\n",
+        "12       2007  0.500000           4   3.219869 -0.268686\n",
+        "13       2008  0.608696           3   2.719869 -0.124712\n",
+        "14       2009  0.639594           2   2.111173 -0.053441\n",
+        "15       2010  0.746479           1   1.471579  0.010690\n",
+        "16       2011  0.725100           0   0.725100  0.000000"
+       ]
+      }
+     ],
+     "prompt_number": 528
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.text()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "TypeError",
+       "evalue": "text() takes at least 3 arguments (0 given)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-9-d40150873c3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mTypeError\u001b[0m: text() takes at least 3 arguments (0 given)"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df = sheep[[\"Interface_hepatitis\", \"AgeAtDeath\", \"Entropy\"]].dropna()\n",
+      "ols = MixedLM.from_formula(rstr(\"Interface_hepatitis\", [\"Entropy\"]), data=df, groups=df.AgeAtDeath).fit()\n",
+      "ols.summary()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "/Users/qcaudron/anaconda/lib/python2.7/site-packages/statsmodels-0.7.0-py2.7-macosx-10.5-x86_64.egg/statsmodels/regression/mixed_linear_model.py:1688: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
+        "  warnings.warn(msg, ConvergenceWarning)\n"
+       ]
+      },
+      {
+       "html": [
+        "<table class=\"simpletable\">\n",
+        "<tr>\n",
+        "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td> <td>Interface_hepatitis</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <td>No. Observations:</td>   <td>85</td>          <td>Method:</td>              <td>REML</td>        \n",
+        "</tr>\n",
+        "<tr>\n",
+        "     <td>No. Groups:</td>      <td>11</td>          <td>Scale:</td>              <td>0.0903</td>       \n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <td>Min. group size:</td>     <td>1</td>        <td>Likelihood:</td>          <td>-20.8254</td>      \n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <td>Max. group size:</td>    <td>23</td>        <td>Converged:</td>              <td>Yes</td>        \n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <td>Mean group size:</td>    <td>7.7</td>            <td></td>                    <td></td>          \n",
+        "</tr>\n",
+        "</table>\n",
+        "<table class=\"simpletable\">\n",
+        "<tr>\n",
+        "        <td></td>        <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Intercept</th>    <td>-0.170</td>   <td>0.154</td>  <td>-1.101</td> <td>0.271</td> <td>-0.473</td>  <td>0.133</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Entropy</th>       <td>0.511</td>   <td>0.206</td>   <td>2.485</td> <td>0.013</td>  <td>0.108</td>  <td>0.914</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Intercept RE</th>  <td>0.001</td>   <td>0.015</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
+        "</tr>\n",
+        "</table>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 524,
+       "text": [
+        "<class 'statsmodels.iolib.summary2.Summary'>\n",
+        "\"\"\"\n",
+        "              Mixed Linear Model Regression Results\n",
+        "=================================================================\n",
+        "Model:            MixedLM Dependent Variable: Interface_hepatitis\n",
+        "No. Observations: 85      Method:             REML               \n",
+        "No. Groups:       11      Scale:              0.0903             \n",
+        "Min. group size:  1       Likelihood:         -20.8254           \n",
+        "Max. group size:  23      Converged:          Yes                \n",
+        "Mean group size:  7.7                                            \n",
+        "-------------------------------------------------------------------\n",
+        "               Coef.    Std.Err.     z      P>|z|   [0.025   0.975]\n",
+        "-------------------------------------------------------------------\n",
+        "Intercept      -0.170      0.154   -1.101   0.271   -0.473    0.133\n",
+        "Entropy         0.511      0.206    2.485   0.013    0.108    0.914\n",
+        "Intercept RE    0.001      0.015                                   \n",
+        "=================================================================\n",
+        "\n",
+        "\"\"\""
+       ]
+      }
+     ],
+     "prompt_number": 524
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print mmR2(df, ols)\n",
+      "print ols.random_effects.abs().mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.0700322678299\n",
+        "Intercept RE    0.003643\n",
+        "dtype: float64\n"
+       ]
+      }
+     ],
+     "prompt_number": 525
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print ols.summary()\n",
+      "\n",
+      "print ols.bse\n",
+      "print ols.bse_re\n",
+      "print ols.bse_fe"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "         Mixed Linear Model Regression Results\n",
+        "=======================================================\n",
+        "Model:              MixedLM Dependent Variable: Weight \n",
+        "No. Observations:   62      Method:             REML   \n",
+        "No. Groups:         11      Scale:              0.0200 \n",
+        "Min. group size:    1       Likelihood:         19.0089\n",
+        "Max. group size:    21      Converged:          Yes    \n",
+        "Mean group size:    5.6                                \n",
+        "-------------------------------------------------------\n",
+        "             Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
+        "-------------------------------------------------------\n",
+        "Intercept     0.502    0.076  6.561 0.000  0.352  0.651\n",
+        "Inflammation  0.401    0.192  2.087 0.037  0.024  0.778\n",
+        "FociSize     -0.405    0.187 -2.168 0.030 -0.771 -0.039\n",
+        "Intercept RE  0.041    0.198                           \n",
+        "=======================================================\n",
+        "\n",
+        "Intercept       0.076461\n",
+        "Inflammation    0.192210\n",
+        "FociSize        0.186801\n",
+        "Intercept RE    1.403026\n",
+        "dtype: float64\n",
+        "Intercept RE    0.19819\n",
+        "dtype: float64\n",
+        "Intercept       0.076461\n",
+        "Inflammation    0.192210\n",
+        "FociSize        0.186801\n",
+        "dtype: float64\n"
+       ]
+      }
+     ],
+     "prompt_number": 231
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "histo.columns"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 526,
+       "text": [
+        "Index([u'Sheep', u'Lobular_collapse', u'Interface_hepatitis', u'Confluent_necrosis', u'Ln_ap_ri', u'Portal_inflammation', u'BD_hyperplasia', u'Fibrosis', u'TawfikTotal', u'Mean_hep_size', u'Min_hep_size', u'Max_hep_size'], dtype='object')"
+       ]
+      }
+     ],
+     "prompt_number": 526
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y1, y2 = mmPredict(df, ols)\n",
+      "plt.scatter(y1, y2)\n",
+      "plt.plot(y1,y1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 382,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x141d05d10>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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mHDTqDgDMmw80p5xyXE455bgcf/yuzM4u3NFutXbs2Hf78I3ZvXvbbWFs3MzM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\n9lrrzWvtF+2xWB3SH7zwEbn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ggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x141d05050>"
+       ]
+      }
+     ],
+     "prompt_number": 382
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>Sheep</th>\n",
+        "      <th>Image</th>\n",
+        "      <th>Lacunarity</th>\n",
+        "      <th>Entropy</th>\n",
+        "      <th>FociCount</th>\n",
+        "      <th>FociSize</th>\n",
+        "      <th>Scale</th>\n",
+        "      <th>Directionality</th>\n",
+        "      <th>TissueToSinusoid</th>\n",
+        "      <th>LobularCollapse</th>\n",
+        "      <th>...</th>\n",
+        "      <th>Sex</th>\n",
+        "      <th>AgeAtDeath</th>\n",
+        "      <th>Weight</th>\n",
+        "      <th>Foreleg</th>\n",
+        "      <th>Hindleg</th>\n",
+        "      <th>BirthYear</th>\n",
+        "      <th>E</th>\n",
+        "      <th>CES</th>\n",
+        "      <th>RES</th>\n",
+        "      <th>Inflammation</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>0 rows \u00d7 31 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 33,
+       "text": [
+        "Empty DataFrame\n",
+        "Columns: [Sheep, Image, Lacunarity, Entropy, FociCount, FociSize, Scale, Directionality, TissueToSinusoid, LobularCollapse, InterfaceHepatitis, ConfluentNecrosis, LnApRi, PortalInflammation, BDHyperplasia, Fibrosis, TawfikTotal, MeanHepSize, MinHepSize, MaxHepSize, ID, Sex, AgeAtDeath, Weight, Foreleg, Hindleg, BirthYear, E, CES, RES, Inflammation]\n",
+        "Index: []\n",
+        "\n",
+        "[0 rows x 31 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ols = pd.stats.api.ols(y = avesc.Portal_inflammation, x = avesc.Inflammation)\n",
+      "print ols.summary"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(avesc.FociSize, avesc.Portal_inflammation)\n",
+      "plt.plot(avesc.FociSize, avesc.FociSize * ols.beta[0] + ols.beta[1])\n",
+      "plt.title(\"p = %.02e\" % ols.p_value[0])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ols2 = pd.stats.api.ols(y = avesc.BD_hyperplasia, x = avesc[[\"FociSize\", \"Directionality\", \"Scale\"]])\n",
+      "print ols2.summary"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "X = [\"FociSize\", \"Directionality\", \"Scale\"]\n",
+      "x = (ols2.beta[-1] + \n",
+      "    ols2.beta[0] * avesc[X[0]] +\n",
+      "    ols2.beta[1] * avesc[X[1]] + \n",
+      "    ols2.beta[2] * avesc[X[2]])\n",
+      "y = avesc.BD_hyperplasia\n",
+      "\n",
+      "plt.scatter(x, y)\n",
+      "#plt.plot([0, 1], [- 0.0504, 1- 0.0504] )\n",
+      "#plt.plot(y, x)\n",
+      "\n",
+      "#plt.scatter(avesc.FociSize, avesc.Portal_inflammation)\n",
+      "#plt.plot(avesc.FociSize, avesc.FociSize * ols.beta[0] + ols.beta[1])\n",
+      "#plt.title(\"p = %.02e\" % ols.p_value[0])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ols3 = pd.stats.api.ols(y = avesc.Portal_inflammation, x = avesc.Inflammation)\n",
+      "plt.scatter(avesc.Inflammation, avesc.Portal_inflammation)\n",
+      "plt.plot(avesc.Inflammation, avesc.Inflammation * ols3.beta[0] + ols3.beta[1])\n",
+      "print ols3.summary"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ols4 = pd.stats.api.ols(y = avesc.QTotal, x = avesc.TawfikTotal)\n",
+      "plt.scatter(avesc.TawfikTotal, avesc.QTotal)\n",
+      "plt.plot(avesc.TawfikTotal, avesc.TawfikTotal * ols4.beta[0] + ols4.beta[1])\n",
+      "print ols4.summary"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df2 = sheep[[\"TawfikTotal\", \"Entropy\", \"AgeAtDeath\"]]\n",
+      "df2.dropna(inplace=True)\n",
+      "ols = MixedLM.from_formula(rstr(\"TawfikTotal\", [\"Entropy\"]), data=df2, groups=\"AgeAtDeath\").fit()\n",
+      "\n",
+      "ols.pvalues[1:-1].values"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 118,
+       "text": [
+        "array([ 0.03245166])"
+       ]
+      }
+     ],
+     "prompt_number": 118
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df = sheep[[\"Inflammation\", \"Mean_hep_size\", \"FociSize\"]].dropna()\n",
+      "df[\"Intercept\"] = np.ones(len(df))\n",
+      "ols = sm.GLS(endog=df.Mean_hep_size, exog=df[[\"Inflammation\", \"FociSize\"]]).fit()\n",
+      "ols.rsquared"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 15,
+       "text": [
+        "0.76761147156602549"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mmR2(df, ols)\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "print \"YO\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print \"YO\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "YO\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y = pca.fit_transform(s[histcols])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mm, mmfits, mmpvals, mmqsum = test_all_linear(sheep, [\"TawfikTotal\"], imagecols, group=\"AgeAtDeath\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df = sheep[delayer([\"TawfikTotal\", \"Inflammation\", \"ResidualES\"])].dropna()\n",
+      "df[\"Intercept\"] = np.ones(len(df))\n",
+      "tt = MixedLM(endog = df.TawfikTotal, exog = df[[\"Inflammation\"]], groups=df.ResidualES).fit()\n",
+      "tt.summary()\n",
+      "#del fibrosis.tables[2]\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "models, fits, pvals, blah = test_all_linear(sheep, [\"Ln_ap_ri\"], imagecols, group=\"AgeAtDeath\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.where(np.array(fits[\"Ln_ap_ri\"]) < (2 + np.min(fits[\"Ln_ap_ri\"])))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "idx = 0\n",
+      "print models[\"Ln_ap_ri\"][idx].summary()\n",
+      "print fits[\"Ln_ap_ri\"][idx]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a.model.endog_names\n",
+      "\n",
+      "plt.scatter(y, yhat, c=seaborn.color_palette(\"deep\", 8)[0])\n",
+      "plt.plot(y, y, c=seaborn.color_palette(\"deep\", 8)[2])\n",
+      "plt.xlabel(a.model.endog_names)\n",
+      "yl = a.model.exog_names\n",
+      "#yl.remove(\"Intercept\")\n",
+      "plt.ylabel(\", \".join(yl))\n",
+      "plt.title(\"R2 = %.02f\")\n",
+      "st = \"%s : %.03f, p = %.03e.\\n\" * len(yl)\n",
+      "stl = []\n",
+      "for i in range(len(yl)) :\n",
+      "    stl.append(yl[i])\n",
+      "    stl.append(p[dependent][i])\n",
+      "plt.suptitle(st % tuple(delayer([yl[i], p[dependent][i] for i in range(len(yl))\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 420,
+       "text": [
+        "<matplotlib.text.Text at 0x11872d110>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Fk1xjdFONiZYEbs04G11ExBCwHHg6RR/Wi8pdFyVJFWHIkiQ1XWbeExGvoNga/YiI+E1m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\n7sYOmXkvcMrItohYDBwHbGxDjJIkSZI0aW0rsiJ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+       "text": [
+        "<matplotlib.figure.Figure at 0x122780c10>"
+       ]
+      }
+     ],
+     "prompt_number": 420
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a.model.exog_names"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 421,
+       "text": [
+        "['Inflammation', 'FociSize']"
+       ]
+      }
+     ],
+     "prompt_number": 421
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "raw_data[raw_data.TissueToSinusoid == raw_data.TissueToSinusoid.max()]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>Sheep</th>\n",
+        "      <th>Image</th>\n",
+        "      <th>Lacunarity</th>\n",
+        "      <th>Entropy</th>\n",
+        "      <th>FociCount</th>\n",
+        "      <th>FociSize</th>\n",
+        "      <th>Scale</th>\n",
+        "      <th>Directionality</th>\n",
+        "      <th>TissueToSinusoid</th>\n",
+        "      <th>LobularCollapse</th>\n",
+        "      <th>...</th>\n",
+        "      <th>Sex</th>\n",
+        "      <th>AgeAtDeath</th>\n",
+        "      <th>Weight</th>\n",
+        "      <th>Foreleg</th>\n",
+        "      <th>Hindleg</th>\n",
+        "      <th>BirthYear</th>\n",
+        "      <th>E</th>\n",
+        "      <th>CES</th>\n",
+        "      <th>RES</th>\n",
+        "      <th>Inflammation</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1004</th>\n",
+        "      <td> 41</td>\n",
+        "      <td> 116</td>\n",
+        "      <td> 0.128041</td>\n",
+        "      <td> 0.620546</td>\n",
+        "      <td> 13</td>\n",
+        "      <td> 467.384615</td>\n",
+        "      <td> 25</td>\n",
+        "      <td> 0.157612</td>\n",
+        "      <td> 0.56434</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 21.2</td>\n",
+        "      <td> 126</td>\n",
+        "      <td> 182</td>\n",
+        "      <td> 2007</td>\n",
+        "      <td> 0.5</td>\n",
+        "      <td> 3.219869</td>\n",
+        "      <td>-0.268686</td>\n",
+        "      <td> 6076</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>1 rows \u00d7 31 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 45,
+       "text": [
+        "      Sheep  Image  Lacunarity   Entropy  FociCount    FociSize  Scale  \\\n",
+        "1004     41    116    0.128041  0.620546         13  467.384615     25   \n",
+        "\n",
+        "      Directionality  TissueToSinusoid  LobularCollapse      ...       Sex  \\\n",
+        "1004        0.157612           0.56434              NaN      ...         1   \n",
+        "\n",
+        "      AgeAtDeath  Weight  Foreleg  Hindleg  BirthYear    E       CES  \\\n",
+        "1004           4    21.2      126      182       2007  0.5  3.219869   \n",
+        "\n",
+        "           RES  Inflammation  \n",
+        "1004 -0.268686          6076  \n",
+        "\n",
+        "[1 rows x 31 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 45
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df = sheep[[\"MeanHepSize\", \"Directionality\"]].dropna()\n",
+      "df[\"Intercept\"] = np.ones(len(df))\n",
+      "ols = sm.GLS(endog=df.MeanHepSize, exog=df[[\"Directionality\", \"Intercept\"]]).fit()\n",
+      "ols.summary()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<table class=\"simpletable\">\n",
+        "<caption>GLS Regression Results</caption>\n",
+        "<tr>\n",
+        "  <th>Dep. Variable:</th>       <td>MeanHepSize</td>   <th>  R-squared:         </th> <td>   0.002</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Model:</th>                   <td>GLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.008</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.2341</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Date:</th>             <td>Mon, 24 Nov 2014</td> <th>  Prob (F-statistic):</th>  <td> 0.630</td> \n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Time:</th>                 <td>14:51:02</td>     <th>  Log-Likelihood:    </th> <td>  47.035</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>No. Observations:</th>      <td>    97</td>      <th>  AIC:               </th> <td>  -90.07</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Df Residuals:</th>          <td>    95</td>      <th>  BIC:               </th> <td>  -84.92</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+        "</tr>\n",
+        "</table>\n",
+        "<table class=\"simpletable\">\n",
+        "<tr>\n",
+        "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Directionality</th> <td>    0.0364</td> <td>    0.075</td> <td>    0.484</td> <td> 0.630</td> <td>   -0.113     0.186</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Intercept</th>      <td>    0.5481</td> <td>    0.027</td> <td>   20.566</td> <td> 0.000</td> <td>    0.495     0.601</td>\n",
+        "</tr>\n",
+        "</table>\n",
+        "<table class=\"simpletable\">\n",
+        "<tr>\n",
+        "  <th>Omnibus:</th>       <td> 6.787</td> <th>  Durbin-Watson:     </th> <td>   2.185</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Prob(Omnibus):</th> <td> 0.034</td> <th>  Jarque-Bera (JB):  </th> <td>  11.331</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Skew:</th>          <td>-0.138</td> <th>  Prob(JB):          </th> <td> 0.00346</td>\n",
+        "</tr>\n",
+        "<tr>\n",
+        "  <th>Kurtosis:</th>      <td> 4.651</td> <th>  Cond. No.          </th> <td>    5.35</td>\n",
+        "</tr>\n",
+        "</table>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 50,
+       "text": [
+        "<class 'statsmodels.iolib.summary.Summary'>\n",
+        "\"\"\"\n",
+        "                            GLS Regression Results                            \n",
+        "==============================================================================\n",
+        "Dep. Variable:            MeanHepSize   R-squared:                       0.002\n",
+        "Model:                            GLS   Adj. R-squared:                 -0.008\n",
+        "Method:                 Least Squares   F-statistic:                    0.2341\n",
+        "Date:                Mon, 24 Nov 2014   Prob (F-statistic):              0.630\n",
+        "Time:                        14:51:02   Log-Likelihood:                 47.035\n",
+        "No. Observations:                  97   AIC:                            -90.07\n",
+        "Df Residuals:                      95   BIC:                            -84.92\n",
+        "Df Model:                           1                                         \n",
+        "Covariance Type:            nonrobust                                         \n",
+        "==================================================================================\n",
+        "                     coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
+        "----------------------------------------------------------------------------------\n",
+        "Directionality     0.0364      0.075      0.484      0.630        -0.113     0.186\n",
+        "Intercept          0.5481      0.027     20.566      0.000         0.495     0.601\n",
+        "==============================================================================\n",
+        "Omnibus:                        6.787   Durbin-Watson:                   2.185\n",
+        "Prob(Omnibus):                  0.034   Jarque-Bera (JB):               11.331\n",
+        "Skew:                          -0.138   Prob(JB):                      0.00346\n",
+        "Kurtosis:                       4.651   Cond. No.                         5.35\n",
+        "==============================================================================\n",
+        "\n",
+        "Warnings:\n",
+        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+        "\"\"\""
+       ]
+      }
+     ],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 54,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x1189a4210>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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lYQgAAGilm4d5cinlSJJ3d9qZSfKhWuvPd+qel+Q9Sa4kmU/y2lrr/HDdBQAAaMawZ4Ye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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1189ad1d0>"
+       ]
+      }
+     ],
+     "prompt_number": 54
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file