{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 14: Categorical Predictors\n", "\n", "Import the packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "import seaborn as sns\n", "from scipy import stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two-level factor\n", "\n", "Read in the data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cpaptsdcsa
12.047869.71365Abused
20.838956.16933Abused
3-0.2413915.15926Abused
4-1.1146111.31277Abused
52.014689.95384Abused
\n", "
" ], "text/plain": [ " cpa ptsd csa\n", "1 2.04786 9.71365 Abused\n", "2 0.83895 6.16933 Abused\n", "3 -0.24139 15.15926 Abused\n", "4 -1.11461 11.31277 Abused\n", "5 2.01468 9.95384 Abused" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sexab = pd.read_csv(\"data/sexab.csv\", index_col=0)\n", "sexab.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct something similar to the `by` function summary from LMR." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cpaptsd
minmedianmaxminmedianmax
csa
Abused-1.12.68.66.011.319.0
NotAbused-3.11.35.0-3.35.810.9
\n", "
" ], "text/plain": [ " cpa ptsd \n", " min median max min median max\n", "csa \n", "Abused -1.1 2.6 8.6 6.0 11.3 19.0\n", "NotAbused -3.1 1.3 5.0 -3.3 5.8 10.9" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lfuncs = ['min','median','max']\n", "sexab.groupby('csa').agg({'cpa': lfuncs,'ptsd': lfuncs}).round(1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sexab.boxplot('ptsd',by='csa')\n", "plt.suptitle(\"\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.7/site-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n", " warnings.warn(msg, UserWarning)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(x_vars=\"cpa\", y_vars=\"ptsd\", data=sexab, hue=\"csa\",size=5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=8.938657095668173, pvalue=2.1719334389283135e-13)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.ttest_ind(sexab.ptsd[sexab.csa == 'Abused'], sexab.ptsd[sexab.csa == 'NotAbused'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define dummy variables and including an intercept does fit a model but the meaning of the coefficients and standard errors are problematic. Look out for the *design matrix is singular* warning at the end." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:18 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
const 5.5457 0.270 20.526 0.000 5.007 6.084
x1 6.3954 0.403 15.874 0.000 5.593 7.198
x2 -0.8498 0.450 -1.888 0.063 -1.747 0.047
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 9.34e+15


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.33e-30. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:18 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 5.5457 0.270 20.526 0.000 5.007 6.084\n", "x1 6.3954 0.403 15.874 0.000 5.593 7.198\n", "x2 -0.8498 0.450 -1.888 0.063 -1.747 0.047\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 9.34e+15\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The smallest eigenvalue is 1.33e-30. This might indicate that there are\n", "strong multicollinearity problems or that the design matrix is singular.\n", "\"\"\"" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1 = (sexab.csa == 'Abused').astype(int)\n", "df2 = (sexab.csa == 'NotAbused').astype(int)\n", "X = np.column_stack((df1,df2))\n", "lmod = sm.OLS(sexab.ptsd,sm.add_constant(X)).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use only one of the dummy variables:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:18 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
const 11.9411 0.518 23.067 0.000 10.910 12.973
csa -7.2452 0.811 -8.939 0.000 -8.860 -5.630
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 2.46


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:18 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 11.9411 0.518 23.067 0.000 10.910 12.973\n", "csa -7.2452 0.811 -8.939 0.000 -8.860 -5.630\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 2.46\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = sm.OLS(sexab.ptsd,sm.add_constant(df2)).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we can just not have an intercept term:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:18 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
x1 11.9411 0.518 23.067 0.000 10.910 12.973
x2 4.6959 0.624 7.529 0.000 3.453 5.939
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 1.20


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:18 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 11.9411 0.518 23.067 0.000 10.910 12.973\n", "x2 4.6959 0.624 7.529 0.000 3.453 5.939\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 1.20\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = sm.OLS(sexab.ptsd,X).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the formula method produces R-like results" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:18 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 11.9411 0.518 23.067 0.000 10.910 12.973
csa[T.NotAbused] -7.2452 0.811 -8.939 0.000 -8.860 -5.630
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 2.46


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:18 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "====================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------------\n", "Intercept 11.9411 0.518 23.067 0.000 10.910 12.973\n", "csa[T.NotAbused] -7.2452 0.811 -8.939 0.000 -8.860 -5.630\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 2.46\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('ptsd ~ csa', sexab).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can view the design matrix: (picking out the first two rows of each level of csa)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 0.],\n", " [1., 0.],\n", " [1., 1.],\n", " [1., 1.]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import patsy\n", "selcols = [0,1,45,46]\n", "patsy.dmatrix('~ csa', sexab)[selcols,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can check the types of variables:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(dtype('O'), dtype('float64'))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sexab.csa.dtype, sexab.ptsd.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pandas` has a way to create the dummy variables:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:19 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 4.6959 0.624 7.529 0.000 3.453 5.939
Abused 7.2452 0.811 8.939 0.000 5.630 8.860
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 2.89


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:19 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 4.6959 0.624 7.529 0.000 3.453 5.939\n", "Abused 7.2452 0.811 8.939 0.000 5.630 8.860\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 2.89\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sac = pd.concat([sexab,pd.get_dummies(sexab.csa)],axis=1)\n", "lmod = smf.ols('ptsd ~ Abused', sac).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we can set the reference level to NotAbused:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:19 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 4.6959 0.624 7.529 0.000 3.453 5.939
C(csa, Treatment(reference=\"NotAbused\"))[T.Abused] 7.2452 0.811 8.939 0.000 5.630 8.860
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 2.89


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:19 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "======================================================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------------------------------------\n", "Intercept 4.6959 0.624 7.529 0.000 3.453 5.939\n", "C(csa, Treatment(reference=\"NotAbused\"))[T.Abused] 7.2452 0.811 8.939 0.000 5.630 8.860\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 2.89\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('ptsd ~ C(csa,Treatment(reference=\"NotAbused\"))', sexab).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Factors and Quantitative predictors" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.583
Model: OLS Adj. R-squared: 0.565
Method: Least Squares F-statistic: 33.53
Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.13e-13
Time: 15:59:19 Log-Likelihood: -196.04
No. Observations: 76 AIC: 400.1
Df Residuals: 72 BIC: 409.4
Df Model: 3
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 10.5571 0.806 13.094 0.000 8.950 12.164
csa[T.NotAbused] -6.8612 1.075 -6.384 0.000 -9.004 -4.719
cpa 0.4500 0.208 2.159 0.034 0.034 0.866
csa[T.NotAbused]:cpa 0.3140 0.368 0.852 0.397 -0.421 1.049
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.273 Durbin-Watson: 1.837
Prob(Omnibus): 0.529 Jarque-Bera (JB): 1.083
Skew: -0.069 Prob(JB): 0.582
Kurtosis: 2.432 Cond. No. 12.0


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.583\n", "Model: OLS Adj. R-squared: 0.565\n", "Method: Least Squares F-statistic: 33.53\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.13e-13\n", "Time: 15:59:19 Log-Likelihood: -196.04\n", "No. Observations: 76 AIC: 400.1\n", "Df Residuals: 72 BIC: 409.4\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------\n", "Intercept 10.5571 0.806 13.094 0.000 8.950 12.164\n", "csa[T.NotAbused] -6.8612 1.075 -6.384 0.000 -9.004 -4.719\n", "cpa 0.4500 0.208 2.159 0.034 0.034 0.866\n", "csa[T.NotAbused]:cpa 0.3140 0.368 0.852 0.397 -0.421 1.049\n", "==============================================================================\n", "Omnibus: 1.273 Durbin-Watson: 1.837\n", "Prob(Omnibus): 0.529 Jarque-Bera (JB): 1.083\n", "Skew: -0.069 Prob(JB): 0.582\n", "Kurtosis: 2.432 Cond. No. 12.0\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod4 = smf.ols('ptsd ~ csa*cpa', sexab).fit()\n", "lmod4.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at a chunk from the design matrix:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0. , 3.0775 , 0. ],\n", " [ 1. , 0. , 5.26785, 0. ],\n", " [ 1. , 0. , 3.41136, 0. ],\n", " [ 1. , 0. , 1.35316, 0. ],\n", " [ 1. , 0. , 5.11921, 0. ],\n", " [ 1. , 1. , 1.49181, 1.49181],\n", " [ 1. , 1. , 0.60961, 0.60961],\n", " [ 1. , 1. , 1.43335, 1.43335],\n", " [ 1. , 1. , -0.33664, -0.33664],\n", " [ 1. , 1. , -3.12036, -3.12036]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patsy.dmatrix('~ csa*cpa', sexab)[40:50,]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the fit by group - seem like a lot of work!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "abused = (sexab.csa == \"Abused\")\n", "plt.scatter(sexab.cpa[abused], sexab.ptsd[abused], marker='x',label=\"abused\")\n", "xl,xu = [-3, 9]\n", "a, b = (lmod4.params[0], lmod4.params[2])\n", "plt.plot([xl,xu], [a+xl*b,a+xu*b])\n", "plt.scatter(sexab.cpa[~abused], sexab.ptsd[~abused], marker='o',label=\"not abused\")\n", "a, b = (lmod4.params[0]+lmod4.params[1], lmod4.params[2]+lmod4.params[3])\n", "plt.plot([xl,xu], [a+xl*b,a+xu*b])\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can produce essentially the same plot but the regression lines are fit independently to the groups. In this case, the fits will be identical." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lmplot(x=\"cpa\", y=\"ptsd\", hue=\"csa\", data=sexab, ci=None)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.579
Model: OLS Adj. R-squared: 0.567
Method: Least Squares F-statistic: 50.12
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.00e-14
Time: 15:59:19 Log-Likelihood: -196.43
No. Observations: 76 AIC: 398.9
Df Residuals: 73 BIC: 405.8
Df Model: 2
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 10.2480 0.719 14.260 0.000 8.816 11.680
csa[T.NotAbused] -6.2728 0.822 -7.632 0.000 -7.911 -4.635
cpa 0.5506 0.172 3.209 0.002 0.209 0.893
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Omnibus: 0.879 Durbin-Watson: 1.860
Prob(Omnibus): 0.644 Jarque-Bera (JB): 0.878
Skew: -0.070 Prob(JB): 0.645
Kurtosis: 2.492 Cond. No. 9.14


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.579\n", "Model: OLS Adj. R-squared: 0.567\n", "Method: Least Squares F-statistic: 50.12\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.00e-14\n", "Time: 15:59:19 Log-Likelihood: -196.43\n", "No. Observations: 76 AIC: 398.9\n", "Df Residuals: 73 BIC: 405.8\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "====================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------------\n", "Intercept 10.2480 0.719 14.260 0.000 8.816 11.680\n", "csa[T.NotAbused] -6.2728 0.822 -7.632 0.000 -7.911 -4.635\n", "cpa 0.5506 0.172 3.209 0.002 0.209 0.893\n", "==============================================================================\n", "Omnibus: 0.879 Durbin-Watson: 1.860\n", "Prob(Omnibus): 0.644 Jarque-Bera (JB): 0.878\n", "Skew: -0.070 Prob(JB): 0.645\n", "Kurtosis: 2.492 Cond. No. 9.14\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod3 = smf.ols('ptsd ~ csa+cpa', sexab).fit()\n", "lmod3.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No shortcut to producing the plot this time." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "abused = (sexab.csa == \"Abused\")\n", "plt.scatter(sexab.cpa[abused], sexab.ptsd[abused], marker='x',label=\"abused\")\n", "xl,xu = [-3, 9]\n", "a, b = (lmod3.params[0], lmod3.params[2])\n", "plt.plot([xl,xu], [a+xl*b,a+xu*b])\n", "plt.scatter(sexab.cpa[~abused], sexab.ptsd[~abused], marker='o',label=\"not abused\")\n", "a, b = (lmod3.params[0]+lmod4.params[1], lmod3.params[2])\n", "plt.plot([xl,xu], [a+xl*b,a+xu*b])\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the confidence intervals:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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01
Intercept8.81571211.680361
csa[T.NotAbused]-7.910809-4.634696
cpa0.2085840.892520
\n", "
" ], "text/plain": [ " 0 1\n", "Intercept 8.815712 11.680361\n", "csa[T.NotAbused] -7.910809 -4.634696\n", "cpa 0.208584 0.892520" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod3.conf_int()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.residplot(lmod3.fittedvalues, lmod3.resid, lowess=True)\n", "plt.xlabel(\"Fitted values\")\n", "plt.ylabel(\"Residuals\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.242
Model: OLS Adj. R-squared: 0.232
Method: Least Squares F-statistic: 23.67
Date: Tue, 25 Sep 2018 Prob (F-statistic): 6.27e-06
Time: 15:59:19 Log-Likelihood: -218.72
No. Observations: 76 AIC: 441.4
Df Residuals: 74 BIC: 446.1
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 6.5523 0.707 9.265 0.000 5.143 7.962
cpa 1.0334 0.212 4.865 0.000 0.610 1.457
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Omnibus: 0.484 Durbin-Watson: 1.181
Prob(Omnibus): 0.785 Jarque-Bera (JB): 0.176
Skew: 0.103 Prob(JB): 0.916
Kurtosis: 3.114 Cond. No. 4.93


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.242\n", "Model: OLS Adj. R-squared: 0.232\n", "Method: Least Squares F-statistic: 23.67\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 6.27e-06\n", "Time: 15:59:19 Log-Likelihood: -218.72\n", "No. Observations: 76 AIC: 441.4\n", "Df Residuals: 74 BIC: 446.1\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 6.5523 0.707 9.265 0.000 5.143 7.962\n", "cpa 1.0334 0.212 4.865 0.000 0.610 1.457\n", "==============================================================================\n", "Omnibus: 0.484 Durbin-Watson: 1.181\n", "Prob(Omnibus): 0.785 Jarque-Bera (JB): 0.176\n", "Skew: 0.103 Prob(JB): 0.916\n", "Kurtosis: 3.114 Cond. No. 4.93\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod1 = smf.ols('ptsd ~ cpa', sexab).fit()\n", "lmod1.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretation with interaction terms" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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InsulTempGas
1Before-0.87.2
2Before-0.76.9
3Before0.46.4
4Before2.56.0
5Before2.95.8
\n", "
" ], "text/plain": [ " Insul Temp Gas\n", "1 Before -0.8 7.2\n", "2 Before -0.7 6.9\n", "3 Before 0.4 6.4\n", "4 Before 2.5 6.0\n", "5 Before 2.9 5.8" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws = pd.read_csv(\"data/whiteside.csv\", index_col=0)\n", "ws.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lmplot(x=\"Temp\", y=\"Gas\", col=\"Insul\", data=ws)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Gas R-squared: 0.928
Model: OLS Adj. R-squared: 0.924
Method: Least Squares F-statistic: 222.3
Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.23e-29
Time: 15:59:19 Log-Likelihood: -14.100
No. Observations: 56 AIC: 36.20
Df Residuals: 52 BIC: 44.30
Df Model: 3
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 4.7238 0.118 40.000 0.000 4.487 4.961
Insul[T.Before] 2.1300 0.180 11.827 0.000 1.769 2.491
Temp -0.2779 0.023 -12.124 0.000 -0.324 -0.232
Temp:Insul[T.Before] -0.1153 0.032 -3.591 0.001 -0.180 -0.051
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 6.016 Durbin-Watson: 1.854
Prob(Omnibus): 0.049 Jarque-Bera (JB): 4.998
Skew: -0.626 Prob(JB): 0.0822
Kurtosis: 3.757 Cond. No. 30.9


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Gas R-squared: 0.928\n", "Model: OLS Adj. R-squared: 0.924\n", "Method: Least Squares F-statistic: 222.3\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.23e-29\n", "Time: 15:59:19 Log-Likelihood: -14.100\n", "No. Observations: 56 AIC: 36.20\n", "Df Residuals: 52 BIC: 44.30\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------\n", "Intercept 4.7238 0.118 40.000 0.000 4.487 4.961\n", "Insul[T.Before] 2.1300 0.180 11.827 0.000 1.769 2.491\n", "Temp -0.2779 0.023 -12.124 0.000 -0.324 -0.232\n", "Temp:Insul[T.Before] -0.1153 0.032 -3.591 0.001 -0.180 -0.051\n", "==============================================================================\n", "Omnibus: 6.016 Durbin-Watson: 1.854\n", "Prob(Omnibus): 0.049 Jarque-Bera (JB): 4.998\n", "Skew: -0.626 Prob(JB): 0.0822\n", "Kurtosis: 3.757 Cond. No. 30.9\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('Gas ~ Temp*Insul', ws).fit()\n", "lmod.summary()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.875" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws.Temp.mean()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Gas R-squared: 0.928
Model: OLS Adj. R-squared: 0.924
Method: Least Squares F-statistic: 222.3
Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.23e-29
Time: 15:59:19 Log-Likelihood: -14.100
No. Observations: 56 AIC: 36.20
Df Residuals: 52 BIC: 44.30
Df Model: 3
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 3.3689 0.060 56.409 0.000 3.249 3.489
Insul[T.Before] 1.5679 0.088 17.875 0.000 1.392 1.744
cTemp -0.2779 0.023 -12.124 0.000 -0.324 -0.232
cTemp:Insul[T.Before] -0.1153 0.032 -3.591 0.001 -0.180 -0.051
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 6.016 Durbin-Watson: 1.854
Prob(Omnibus): 0.049 Jarque-Bera (JB): 4.998
Skew: -0.626 Prob(JB): 0.0822
Kurtosis: 3.757 Cond. No. 7.17


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Gas R-squared: 0.928\n", "Model: OLS Adj. R-squared: 0.924\n", "Method: Least Squares F-statistic: 222.3\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.23e-29\n", "Time: 15:59:19 Log-Likelihood: -14.100\n", "No. Observations: 56 AIC: 36.20\n", "Df Residuals: 52 BIC: 44.30\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "=========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------------\n", "Intercept 3.3689 0.060 56.409 0.000 3.249 3.489\n", "Insul[T.Before] 1.5679 0.088 17.875 0.000 1.392 1.744\n", "cTemp -0.2779 0.023 -12.124 0.000 -0.324 -0.232\n", "cTemp:Insul[T.Before] -0.1153 0.032 -3.591 0.001 -0.180 -0.051\n", "==============================================================================\n", "Omnibus: 6.016 Durbin-Watson: 1.854\n", "Prob(Omnibus): 0.049 Jarque-Bera (JB): 4.998\n", "Skew: -0.626 Prob(JB): 0.0822\n", "Kurtosis: 3.757 Cond. No. 7.17\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws['cTemp'] = ws.Temp - ws.Temp.mean()\n", "lmod = smf.ols('Gas ~ cTemp*Insul', ws).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Factors with more than two levels" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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thoraxlongevityactivity
10.6837many
20.6849many
30.7246many
40.7263many
50.7639many
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" ], "text/plain": [ " thorax longevity activity\n", "1 0.68 37 many\n", "2 0.68 49 many\n", "3 0.72 46 many\n", "4 0.72 63 many\n", "5 0.76 39 many" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ff = pd.read_csv(\"data/fruitfly.csv\", index_col=0)\n", "ff.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.7/site-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n", " warnings.warn(msg, UserWarning)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(x_vars=\"thorax\", y_vars=\"longevity\", data=ff, hue=\"activity\",size=5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.7/site-packages/seaborn/regression.py:546: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", " warnings.warn(msg, UserWarning)\n", "/anaconda/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lmplot(x=\"thorax\", y=\"longevity\", data=ff, col=\"activity\",size=5)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the model" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: longevity R-squared: 0.653
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 23.88
Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.89e-22
Time: 15:59:21 Log-Likelihood: -464.79
No. Observations: 124 AIC: 949.6
Df Residuals: 114 BIC: 977.8
Df Model: 9
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept -61.2800 22.440 -2.731 0.007 -105.734 -16.826
activity[T.isolated] 11.0380 31.287 0.353 0.725 -50.940 73.017
activity[T.low] 3.2879 34.383 0.096 0.924 -64.824 71.400
activity[T.many] 9.8986 32.961 0.300 0.764 -55.398 75.195
activity[T.one] 17.5552 34.286 0.512 0.610 -50.364 85.475
thorax 125.0000 27.922 4.477 0.000 69.687 180.313
thorax:activity[T.isolated] 11.1268 38.120 0.292 0.771 -64.389 86.642
thorax:activity[T.low] 12.0011 41.718 0.288 0.774 -70.641 94.643
thorax:activity[T.many] 17.6746 40.686 0.434 0.665 -62.924 98.273
thorax:activity[T.one] 6.4496 41.937 0.154 0.878 -76.627 89.527
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Omnibus: 2.091 Durbin-Watson: 1.957
Prob(Omnibus): 0.352 Jarque-Bera (JB): 1.710
Skew: 0.281 Prob(JB): 0.425
Kurtosis: 3.126 Cond. No. 127.


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: longevity R-squared: 0.653\n", "Model: OLS Adj. R-squared: 0.626\n", "Method: Least Squares F-statistic: 23.88\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.89e-22\n", "Time: 15:59:21 Log-Likelihood: -464.79\n", "No. Observations: 124 AIC: 949.6\n", "Df Residuals: 114 BIC: 977.8\n", "Df Model: 9 \n", "Covariance Type: nonrobust \n", "===============================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------------------\n", "Intercept -61.2800 22.440 -2.731 0.007 -105.734 -16.826\n", "activity[T.isolated] 11.0380 31.287 0.353 0.725 -50.940 73.017\n", "activity[T.low] 3.2879 34.383 0.096 0.924 -64.824 71.400\n", "activity[T.many] 9.8986 32.961 0.300 0.764 -55.398 75.195\n", "activity[T.one] 17.5552 34.286 0.512 0.610 -50.364 85.475\n", "thorax 125.0000 27.922 4.477 0.000 69.687 180.313\n", "thorax:activity[T.isolated] 11.1268 38.120 0.292 0.771 -64.389 86.642\n", "thorax:activity[T.low] 12.0011 41.718 0.288 0.774 -70.641 94.643\n", "thorax:activity[T.many] 17.6746 40.686 0.434 0.665 -62.924 98.273\n", "thorax:activity[T.one] 6.4496 41.937 0.154 0.878 -76.627 89.527\n", "==============================================================================\n", "Omnibus: 2.091 Durbin-Watson: 1.957\n", "Prob(Omnibus): 0.352 Jarque-Bera (JB): 1.710\n", "Skew: 0.281 Prob(JB): 0.425\n", "Kurtosis: 3.126 Cond. No. 127.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('longevity ~ thorax*activity', ff).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct and show selected rows of the design matrix (one for each level of activity). DataFrame is just for pretty printing." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Interceptactivity[T.isolated]activity[T.low]activity[T.many]activity[T.one]thoraxthorax:activity[T.isolated]thorax:activity[T.low]thorax:activity[T.many]thorax:activity[T.one]
11.00.00.01.00.00.680.00.000.680.00
251.01.00.00.00.00.700.70.000.000.00
491.00.00.00.01.00.640.00.000.000.64
751.00.01.00.00.00.680.00.680.000.00
991.00.00.00.00.00.640.00.000.000.00
\n", "
" ], "text/plain": [ " Intercept activity[T.isolated] activity[T.low] activity[T.many] \\\n", "1 1.0 0.0 0.0 1.0 \n", "25 1.0 1.0 0.0 0.0 \n", "49 1.0 0.0 0.0 0.0 \n", "75 1.0 0.0 1.0 0.0 \n", "99 1.0 0.0 0.0 0.0 \n", "\n", " activity[T.one] thorax thorax:activity[T.isolated] \\\n", "1 0.0 0.68 0.0 \n", "25 0.0 0.70 0.7 \n", "49 1.0 0.64 0.0 \n", "75 0.0 0.68 0.0 \n", "99 0.0 0.64 0.0 \n", "\n", " thorax:activity[T.low] thorax:activity[T.many] thorax:activity[T.one] \n", "1 0.00 0.68 0.00 \n", "25 0.00 0.00 0.00 \n", "49 0.00 0.00 0.64 \n", "75 0.68 0.00 0.00 \n", "99 0.00 0.00 0.00 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mm = patsy.dmatrix('~ thorax*activity', ff)\n", "ii = (1, 25, 49, 75, 99)\n", "pd.DataFrame(mm[ii,:],index=ii,columns=lmod.params.index)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.residplot(lmod.fittedvalues, lmod.resid, lowess=True)\n", "plt.xlabel(\"Fitted values\")\n", "plt.ylabel(\"Residuals\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", 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StVeFQUGwAGBrd69gjUcRyQTlDW3dnHMSr9KPvhzMDBawFxcxhvF0YSP1tnh0Tg7qk6jlEhl28C3BKSgRyXChEHTtGisknBN5lQ9pzz85hcas4GJGsw9f8QQXbRYSkQFNm/azVmuiVkukRbEQeMfMXiFYahwAd78vaVWJSI2LtCQi/RClnL/xBv3oS3s+YjG5XMqjjKXrpnDIyoKSkiAYBg1SMGSaRIJiSfhn2/CPiGSgLZcCd45nKgUUcAQfsIQ9yWckT3AhJdnbUlysYKgrEtmPol9NFCIiqRMKRXdcO8fxFgUUcCTv8y1NuYKHeZyL2CZnOx7XqaQ6J5FRT02Amwh2uasfOe7uxyaxLhGpId26wSOPBNePZhr96MtfeI+l7EE3HmI0l7Ce7dR6qMMS6cwOAV8BzYF+BJsJ/SeJNYlIkpSdTW0GDz8Mf/F3mMbRTONYWrCA7gynBQsYm9ONMeO3wx0WL1ZI1FWJBEUjdx8NbHD36e5+MdA+yXWJSDWL3rcagklzR/Ieb3Es73AMezOPa3iQFixgBN1Zz3YasSRAYp3ZG8KXy8zsFOB7oGnyShKR6hYZ9hoZ0XQE/6IffTmOt1nGbvTgAUaRz1q23/QcTZKTiESCYqCZ7QxcDwwDdgKuTWpVIlJtIn0Q7nAYH9CPvvyVqfzA77mW+xjJ5fxGzmbPMdMkOSmVyKinKeGrq4BjkluOiFSX6B3mDuVD+tGXE3iDH2nC9dzDw1y5RUBAEBJXXKHWhJRKZNTT45S/FerFSalIRKos0oo42D9iHAWcxGsspzE3cjcj6EYRO5T7PK3uKuVJ5NTTlKjr9YEzCPopRCQNhUIw8+GPmUIBJ/MqK2jEzdzFQ3RnDTuW+xwFhMSTyKmnSdG3w5sNTU1aRSISU/TppPL8iZn0oy8f8Qo/0ZBbuJPhXLVFQJjBuHEKBklMIi2KsloBzaq7EBGJraKAaMsnFFDAabzMSnbhNgYxjKv5H7/b4rHqg5DKSqSPYjVBH4WFL/8L3JzkukTqtMT2g4CD+JQCCujAZH6mAb0ZwFCuYTU7lft4nWKSrZHIqact/yQRkaSpeD8IOIDPKKCAM3iRn2nA7fRnKNfwKzuX+/hIK2LEiCQVLRktblCY2fZAZ6BN+NAM4Dl3X5/swkTqqh49YofEH5lNX/pxJs/zCzvTlwIepAeraBDz9bKzYexYtSJk68VcwsPM/gjMBf6PYH2nQuAE4H0za2BmA2ukQpE6IBSCxo2Dv/zL64fYn895hrOZzYEcz1T60Yc8FtOfvnFDYtttFRJSdfFaFEOBy9z9zeiDZnY88AUwJ5mFidQV3boFC/OVpw1z6Es/zuFZfuV3DKA393MtP9OwwtdVf4RUl3hBsXvZkABw96lmtoFgPoWIVEEoVLrEd7R9mBsOiGdYww4MpBf3cd2mgLjySvU3SM2JFxRZZradu6+LPmhm9QlWko3T1SYiFYks1OdR6x605iv60J+OTKSIHO7iFu7lelbSCFArQVIj3jLjTwKTzCwvciB8/RlgXDKLEsl0ZfenbsU8xtGFL2nDaUzmbm4ij8X04g7W5jRi/PggUFasUEhIzYvZonD3gWZ2FfCumUVWDlsD3OPuw2qkOpEMFdmfuiXfcDsD6EyItdTnHm5gCDeygiaAWhCSHuIOj3X34cBwM/td+PbqGqkKMLMTgQeBbOAxd7+rpt5bJNm2KZzPEwygC+NZx3bcx3UM4UaW83sAdtwx6LtQQEg6SGSHO9x9dQ2HRDbwEHASwRyOTmbWJv6zRNJXZPjrXraQx+0ivmIfzuEZHqAnzVnETQxhOb8nOxvGj4fVqxUSkj62Zq2nmnAIMN/dFwKY2USgA/BlSqsS2QqhEPS/cCGDNw6iK2PZQD2GcTWDuZkf2G3T48w050HSU0ItihTYA/g26vbS8DGRWiMUgiObLqaoy2V8sbE1nQnxEN3Zi4Vcx/2bhQQEndUKCUlHiSwKmEOwDWozd7/MzFoBraN2vksGK+fYZpsnmVk+kA/QrJkWs5X00vv8QnLHD2Iaj1NMNo9wBXdxC9/H+XsnN7cGCxSphERaFI8D64DDwreXAslevmMpsGfU7aaU2SzJ3Ue5ezt3b9ekSZMklyNSsVAIDm+6hEfsCvqMb8UFjGUkl9OS+VzDsLghoT2qJZ0l0kfRwt3PNbNOAO7+m5mV9xd/dfoP0MrMmgPfAR2B85L8niJb7YWh31J0/R28s3E0AI9xKXdyK0s3+3unfNofQtJdIkGxPryKrAOYWQuCFkbSuPvG8ByO1wmGx45xd60tJennu+/4+qI7OfnNRzGcx7mIQfTi2wT39srNDVoSCglJZ4kERV/gNWBPMwsBRwAXJrMoAHf/J/DPZL+PyNZ4fth3rLhpMF3XjmQvSjYFxBIq7mjIyYFRoxQOUnsksnHRm2b2CdCeoJO5h7uvSHplIuno++/56qK7OPmNUWRTzBNcyCB6UUheQk9XC0Jqo5hBYWZ/KnNoWfiymZk1c/dPkleWSHqZNHwZy28MWhAt2cBYujKIXixirwqfq93lpLaL16K4N859DhxbzbWIpI1QKNhpLvunH7iZwVzJw9RjA+M4n4H0ZiEtEnodtSAkE8RbFPCYmixEJNUi4fDTT9CEH7mFu+nGCLZjHePpwgBuZwEtE3qtbbeFMWMUEJIZEplwVx/oBhxJ0JJ4D3jE3dcmuTaRpIsOB4DGLGcwQ+jOQ9RnLSE6M5DefMPeCb+mVnyVTJPIqKcngdVAZGnxTgT7UZydrKJEqlsoFCztXVgY9Bm4b35/I1ZwA/dwNcPYnt94ivMYwO3Mo3VCr5+VBU8+qXCQzJRIULR29wOjbk8zs8+SVZBIdSjbUogWHRIN+YnruZerGcYOrGEiHRnA7XzFvgm/l4a7SqZLZAmPT82sfeSGmR0KvJ+8kkSqJhSCiy4qPyQidmElA+nFYvK4jTt5lZPYny/ozFMJhURW+P+c3FyFhGS+RFoUhwIXmNmS8O1mwFwz+xxwdz8gadWJVFJkH+rIFqNlNeBnruM+evAgO7GaZzib/vRhDvtX+Nrqe5C6KpGgODHpVYhUg27dgl3hyvY/AOzML1zL/fTkAXbmV57lLPrThy/4Y8zXUzCIBBKZmV1oZrsQrOa6TdRxTbiTtBEKlR8SO/MLPXmAnjxAA1YxiX/Qj758TvkNYW1BKrKlRIbHDiBY22kBpXtCaMKdpIVYndY7sYoePMi13M8u/MILnE4BBczmwM0el5UFJSWaGCcSTyKnns4hWGp8fbKLEUlUrID4Hb9yDUO5jvtoyM+8SAf60ZfZWW0VCCJbKZGg+AJoAPyY5FpEEhIKQX4+FBWVHtuR1VzDUK7nXhryM5P5OwUUMMv+xLhxCgaRqkgkKO4kGCL7BVH7ULj7aUmrSiSGsqOadmQ1VzGcG7iHRqzkZU6lH32ZSTttCCRSTRIJirHAYOBzoCS55YhsKXpWdUQOa+jOQ9zIEJqwglc4mQIKmMHBAGRnw9ixCgmR6pBIUKxw96FJr0SkHGVPM+Wwhm6M4EaG8HuW8yonUkABH3PopudoQT6R6pVIUMw0szuByWx+6knDYyXpevUKQmJ7iriSh7mJu9mVH3mdv1FAAR9y2GaP19wHkeqXSFC0DV+2jzqm4bGSdKEQ/FD4Gz0YyS3cxW78wJscT1/68W8O3/Q4nWYSSa5EJtxpXwqpcRMf/41Zl49iIXexO/9lKsdxFs/xPkdu9jgzhYRIsiXSosDMTgH2A+pHjrl7/2QVJXXY2rUwahRH97yLjr6MtzmGc3ma9/jLFg/VqCaRmpHIzOxHgBzgGOAx4Czg4yTXJXXN2rXw2GNw553w/fd8xVF05Cmmc3S5D9fEOZGak0iL4nB3P8DMZrt7PzO7F3g+2YVJHbFuHYweTVHvO8j5+Tve5f/oy3jeofwznrm5sHhxzZYoUtclEhS/hS+LzOwPwE9A8+SVJHXC+vXBGNY77oBvv2VW1hHczlje5ljAYj5t0KCaK1FEAokExRQzawAMAT4hGPH0aFKrksy1fj088QRrbhvEDj8t4X0Opy9jeKvkOOIFBARDX3WqSaTmJTLqaUD46iQzmwLUd/dVyS1LMs769cHwpEGDoLCQz7MO43YeYyrHU1FAQLDd6IMPJr9MEdlSzK1QzexgM9st6vYFwDPAADNrWJU3NbOzzWyOmZWYWbsy991qZvPN7GszO6Eq7yOp99TYDdzc6DEWbdca8vP5qHBXTuA1Dit5n6n8lURCIjtb242KpFK8PbNHAusBzOwvwF3Ak8AqYFQV3/cL4B/Au9EHzawN0JFgKO6JwAgzy67ie0kNCoUgLw/q2QYusTG0v7A1g1dexnKacBL/pD0f8gYnkEhAQNCS0DwJkdSKFxTZ7r4yfP1cYJS7T3L324GWVXlTd5/r7l+Xc1cHYKK7r3P3RcB84JCqvJfUnFAIrrxsI0cXPsFc9mU0l7CShpzCFA7lI17jJBINCAhGOKklIZJ68fooss1sG3ffCBwH5Cf4vKrYA/gw6vbS8DFJc089uZE3L3yKmT6AVsznE9rydyYzhVOpTDjk5CgcRNJNvF/4E4DpZraCYIjsewBm1pLg9FNcZjYV2K2cu3q5+0uxnlbOMS/nGGaWTzi8mjVrVlE5kizFxbx/1QQOHtmf8/wbPuUgOvAikzmNygQEaBKdSLqKGRTuPsjM3gJ2B95w37RtfRZwdUUv7O7Hb0U9S4E9o243Bb6P8fqjCPeVtGvXrtwwkSQqLoaJE1l14wCOWPY1n3EAZ/A8L3I6akGIZJZ4fRS4+4fu/oK7r4k6Ni+JS4xPBjqa2XZm1hxohZYLSS/Fxfyr+wTm198funRhybJ6nMlztOVTXuQM4oVEVvhfW3Z4eIL6IERqh7hBkSxmdoaZLQUOA14xs9cB3H0OwRDcL4HXgO7uXpyKGiUQGcWUbSVc+run+areHzlyxHms3ZjNWTzLgXzG85yJx/mnlJsL48cHjRB32LgxuFy8WCEhUhskq1M6Lnd/AXghxn2DAC3UkAZCIbj8shJO+m0SU+jH/v+bwxzacA5P8xxnxQ0HnVISyRwpCQqpBUpKeK/nC3zwWz8O4HO+ZF86MoFnOZsS4k9t0QQ5kcySklNPksZKSpje8wXm1m/LIyvOYlvW04mn+COf8zQdKwwJTZATyTwKijos0v+QlQWNGznn7/QSn2b/maMe/AfZG36jM+PZjzlMpFPcgLBw/7U6p0Uyk0491VGhEOTnQ1GR83depmBlAX/iU76hJRcwlqc4j+IE/nk0ahQs1qdwEMlcalHUMZFWRJcuzjFFU/gPBzOZDuzEr3TlCfZlLuO4oNyQaNQo+DErHcm0YoVCQiTTqUVRh4RCkH+Zc/Rv/+RZCjiYGSykORcxhvF0YSP1Yj5XO8uJ1F1qUdQBoRDk5Trju7zK27+15xVOpTEruITHaM3XPMFFcUMiJ0c7y4nUZQqKDFS2k3rCha8zYcnhvMrJ7MoPXMqj7M08xnBJzIBQB7WIROjUU4aJ7qQ+nqn0W9mXw/k3hTTjMkYxlq5sYNstnteoUXC5ciU0a6bF+USklIIiw/S6zWlf9Db96MuRvM8S9uRyHuFxLio3IDSDWkQqolNPmWTaNJ5cchRvcTy5FHIlI2jFN4zi8nJDQqeVRCQRalFkgunToW9fmD6dvbP/QPfi4TzGpaxnu3IfrlaEiFSGWhS12bvvwrHHwtFHw7x5MHQo0x5dwBM53TcLiXr1Np//oJAQkcpQUNRG//oXHHccHHUUzJ3LjC4P0HqbBWT1uJpb+9Wna9cgECLB8PjjwcS4khIt7S0iladTT7XJBx8Ep5imToVdd4X77mPizpdzydU5FBUFDyksDBblU6tBRKqLWhS1wb//DSecAEccAbNnwz33wMKFcO213NK/NCQiioqgV6/UlCoimUdBkc4++ghOOgkOPxw++QSGDGHiHQvJG3Y9WTvmkJcXtCDKs2RJjVYqIhlMQZGOPv4YTj4Z2reHGTNg8GBYtIjQ7jdwyTU7UFgYbCVaWFg6g7qsZs1qtmQRyVwKijQRCsHfd5/BFDsVDj2Ute99zKfn3Emb7ReRdctN5O2/Iz16sMVpJvctw0JrM4lIdVJgxKLyAAALoUlEQVRndhp4deBMdulbwMslU/iJhtzKHYxcexVrXvwd69cHj4l1igmCsMjNDU43afkNEaluCooaFAoFncyRX+gjLvuUkz8u4KTJk1nJLvRiIMO4mtXsBBsTf10tAS4iyaSgqCGli/XBgcyib2E/Tu79IutyGjCI/jxAjyAgKkmnmUQk2dRHUUN69YIWRbOZxD+YRVuOYRp96Ee7Rot5Mvf2hEOiUaPNJ9NpvoSIJJtaFDXh88+5p7AfZzGJVexEP/pwP9eyigbYUhg3rrS1EVGvXhAGkT4KCFoP2p9aRGqaWhTJNGcOnHMOHHAAJ9gbDKA3eSymgH6sogEQ9FV07hy0DMouuzFmjFoPIpJ65u6prqHK2rVr5zNmzEhpDdEd1cfu9iWP5fYn76NnYIcdoEcPnt3zOi68ruFmrQat4ioiqWRmM929XUWPS0mLwsyGmNlXZjbbzF4wswZR991qZvPN7GszOyEV9VVWpKO6fuFXjPfzeGPZ/jT+cApfnHpLMBxp4EDOvrzhFq0GhYSI1AapOvX0JrC/ux8AzANuBTCzNkBHYD/gRGCEmWWnqMbN9p7Oywtul2f0TV8zsqgLc9iPDrzE3dxEHos5dfYdpXuMEoTC4sVaxVVEapeUdGa7+xtRNz8Ezgpf7wBMdPd1wCIzmw8cAvy7hkvcbDgrBBPe8vOD65t+wX/zDfTvz5vfP8Va6nMv1zOEG1lBEwBWar0lEckA6dCZfTHwavj6HsC3UfctDR+rcb16bblcxqZVWefPh65dYZ99YNIkHtvpOpqziJu5e1NIgNZbEpHMkLQWhZlNBXYr565e7v5S+DG9COYgR07qlLfEXbm97WaWD+QDNEvCb+TyVl/diwX0LhwI+4wLxq/27Ak33cSOU3dlTT5QpqNaE+FEJBMkLSjc/fh495tZV+BU4DgvHXq1FNgz6mFNge9jvP4oYBQEo56qXHAZzZqVrq/UnIX0YhBdGctGqwdXXw033wy7BTkYORUVvTyH1lsSkUyRqlFPJwI3A6e5e/QJnslARzPbzsyaA62Aj1NR46BBsE/9xTzKpXxNazoTYuQ2V/HK0IVw//2bQiJCHdUikqlSNTN7OLAd8KYFa2R/6O5XuPscM3sG+JLglFR3dy+u8eoKC+k8fRCdNjzOBrJ5hCsZt8ct9Bj8B85UAIhIHaMJd9GWLIE77gimRJvBZZfBrbfCHinpTxcRSapEJ9xprSeAb78NAmL06M0DomnTVFcmIpJydTsoli6FO++Exx4Ldv+55BK47TbYc8+KnysiUkfUzaD4/vsgIEaNCnqfL744CIjc3FRXJiKSdupWUCxbBnfdBSNHQnExXHhhMKY1Ly/VlYmIpK26ERT//W9pQGzYUBoQzZunujIRkbSX2UHxww8weDA8/HAQEBdcAL17w157pboyEZFaI3ODYs4cOPhgWLcOzj8/CIiWLVNdlYhIrZO5QdGmDVx3XbB4X6tWqa5GRKTWytygMIOBA1NdhYhIrZcOy4yLiEgaU1CIiEhcCgoREYlLQSEiInFlbFCEQsGE66ys4DIUqugZIiJSnowc9RQKQX5+6Z7XhYXBbdCGQiIilZWRLYpevUpDIqKoKDguIiKVk5FBsWRJ5Y6LiEhsGRkUzZpV7riIiMSWkUExaBDk5Gx+LCcnOC4iIpWTkUHRuXOwJ1FubrCSR25ucFsd2SIilZeRo54gCAUFg4hI1WVki0JERKqPgkJEROJSUIiISFwKChERiUtBISIicZm7p7qGKjOz5UBhqusAGgMrUl1EmtB3UUrfRUDfQ6l0+S5y3b1JRQ/KiKBIF2Y2w93bpbqOdKDvopS+i4C+h1K17bvQqScREYlLQSEiInEpKKrXqFQXkEb0XZTSdxHQ91CqVn0X6qMQEZG41KIQEZG4FBTVzMyGmNlXZjbbzF4wswaprilVzOxsM5tjZiVmVmtGeFQXMzvRzL42s/lmdkuq60kVMxtjZj+a2RepriXVzGxPM5tmZnPD/2/0SHVNiVBQVL83gf3d/QBgHnBriutJpS+AfwDvprqQmmZm2cBDwElAG6CTmbVJbVUp8wRwYqqLSBMbgevdfV+gPdC9Nvy7UFBUM3d/w903hm9+CDRNZT2p5O5z3f3rVNeRIocA8919obuvByYCHVJcU0q4+7vAylTXkQ7cfZm7fxK+vhqYC+yR2qoqpqBIrouBV1NdhKTEHsC3UbeXUgt+IUjNMbM8oC3wUWorqVjGblyUTGY2FditnLt6uftL4cf0ImhmhmqytpqWyHdRR1k5xzTEUAAwsx2BSUBPd/811fVUREGxFdz9+Hj3m1lX4FTgOM/w8ccVfRd12FJgz6jbTYHvU1SLpBEzq0cQEiF3fz7V9SRCp56qmZmdCNwMnObuRamuR1LmP0ArM2tuZtsCHYHJKa5JUszMDBgNzHX3+1JdT6IUFNVvOPA74E0zm2Vmj6S6oFQxszPMbClwGPCKmb2e6ppqSnhAw1XA6wQdls+4+5zUVpUaZjYB+DfQ2syWmtklqa4phY4AzgeODf9+mGVmJ6e6qIpoZraIiMSlFoWIiMSloBARkbgUFCIiEpeCQkRE4lJQiIhIXAoKSRtm1ihqyOB/zey78PVfzOzLGq7loOhhi2Z22tauAGtmi82scTnHdzazJ81sQfgnZGa7VKXuGO8f87OYWYGZ3VDd7ymZRUEhacPdf3L3g9z9IOAR4P7w9YOAkup+PzOLtzLBQcCmX67uPtnd76rmEkYDC929hbu3AOYTrLRa3Wris0gGU1BIbZFtZo+G1/B/w8y2BzCzFmb2mpnNNLP3zGyf8PFcM3srvC/IW2bWLHz8CTO7z8ymAYPNbIfwfgn/MbNPzaxDeCZ1f+DccIvmXDO70MyGh19j1/BeI5+Ffw4PH38xXMccM8uP92HMrCXwZ2BA1OH+wIFm1trMjjazKVGPH25mF4av9wnX+4WZjQrP9sXM3jGzwWb2sZnNM7P/q+izlKkp1nd5dvi9PjOzOrdkvCgopPZoBTzk7vsBvwBnho+PAq529z8DNwAjwseHA0+G9wUJAUOjXmtv4Hh3vx7oBbzt7gcDxwBDgHpAH+DpcAvn6TK1DAWmu/uBwJ+AyIzri8N1tAOuMbNGcT5PG2CWuxdHDoSvfwrsW8F3MdzdD3b3/YHtCdYVi9jG3Q8BegJ9w0ucx/ss0WJ9l32AE8Kf97QKapMMpEUBpbZY5O6zwtdnAnnhFTgPB54N/1ENsF348jCCTZMAxgF3R73Ws1G/oP8GnBZ1nr4+0KyCWo4FLoBNv9xXhY9fY2ZnhK/vSRBuP8V4DaP81WTLW3W2rGPM7CYgB2hIEFQvh++LLDI3E8hL4LWCN43/Xb4PPGFmz0S9vtQhCgqpLdZFXS8m+Es6C/gl3I9RkehfymuirhtwZtkNlszs0MoUZ2ZHA8cDh7l7kZm9QxA6scwB2ppZlruXhF8jCzgA+IQgrKJb/PXDj6lP8Jd+O3f/1swKyrxP5HsqpnL/f8f8Lt39ivD3cQowy8wOcvdYASgZSKeepNYKr+O/yMzOhmBlTjM7MHz3BwQrtgJ0Bv4V42VeB66OOs/fNnx8NcHijuV5C7gy/PhsM9sJ2Bn4ORwS+xBscxmv9vkEp5l6Rx3uDbzl7kuAQqCNmW1nZjsDx4UfEwmFFeFWwFnx3ieBzxKpJ+Z3aWYt3P0jd+8DrGDz5dOlDlBQSG3XGbjEzD4j+Cs9st3oNcBFZjabYLXOWJvYDyDok5htZl9Q2rk8jeAX9SwzO7fMc3oQnP75nOAUz37Aa8A24fcbQLANbkUuJliKfL6ZLScIlysA3P1b4BlgNkEfy6fh478AjwKfAy8SLGdekXifJVqs73KImX0e/n7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dfsum_sqmean_sqFPR(>F)
activity4.012269.4671513067.36678826.7278331.200318e-15
thorax1.012368.42083312368.420833107.7735773.565139e-18
thorax:activity4.024.3135926.0783980.0529659.946914e-01
Residual114.013082.983907114.763017NaNNaN
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" ], "text/plain": [ " df sum_sq mean_sq F PR(>F)\n", "activity 4.0 12269.467151 3067.366788 26.727833 1.200318e-15\n", "thorax 1.0 12368.420833 12368.420833 107.773577 3.565139e-18\n", "thorax:activity 4.0 24.313592 6.078398 0.052965 9.946914e-01\n", "Residual 114.0 13082.983907 114.763017 NaN NaN" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.stats.anova_lm(lmod)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n", " return (self.a < x) & (x < self.b)\n", "/anaconda/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n", " return (self.a < x) & (x < self.b)\n", "/anaconda/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n", " cond2 = cond0 & (x <= self.a)\n" ] }, { "data": { "text/html": [ "
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df_residssrdf_diffss_diffFPr(>F)
0118.013107.2975000.0NaNNaNNaN
1114.013082.9839074.024.3135920.0529650.994691
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" ], "text/plain": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 118.0 13107.297500 0.0 NaN NaN NaN\n", "1 114.0 13082.983907 4.0 24.313592 0.052965 0.994691" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmods = smf.ols('longevity ~ thorax+activity', ff).fit()\n", "sm.stats.anova_lm(lmods,lmod)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: np.log(longevity) R-squared: 0.702
Model: OLS Adj. R-squared: 0.690
Method: Least Squares F-statistic: 55.72
Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.81e-29
Time: 15:59:21 Log-Likelihood: 31.033
No. Observations: 124 AIC: -50.07
Df Residuals: 118 BIC: -33.15
Df Model: 5
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 1.4250 0.191 7.477 0.000 1.048 1.802
activity[T.isolated] 0.4193 0.055 7.586 0.000 0.310 0.529
activity[T.low] 0.2954 0.055 5.339 0.000 0.186 0.405
activity[T.many] 0.5072 0.055 9.176 0.000 0.398 0.617
activity[T.one] 0.4710 0.055 8.571 0.000 0.362 0.580
thorax 2.7215 0.233 11.666 0.000 2.259 3.183
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Omnibus: 2.504 Durbin-Watson: 1.883
Prob(Omnibus): 0.286 Jarque-Bera (JB): 2.448
Skew: -0.283 Prob(JB): 0.294
Kurtosis: 2.609 Cond. No. 23.5


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(longevity) R-squared: 0.702\n", "Model: OLS Adj. R-squared: 0.690\n", "Method: Least Squares F-statistic: 55.72\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.81e-29\n", "Time: 15:59:21 Log-Likelihood: 31.033\n", "No. Observations: 124 AIC: -50.07\n", "Df Residuals: 118 BIC: -33.15\n", "Df Model: 5 \n", "Covariance Type: nonrobust \n", "========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------\n", "Intercept 1.4250 0.191 7.477 0.000 1.048 1.802\n", "activity[T.isolated] 0.4193 0.055 7.586 0.000 0.310 0.529\n", "activity[T.low] 0.2954 0.055 5.339 0.000 0.186 0.405\n", "activity[T.many] 0.5072 0.055 9.176 0.000 0.398 0.617\n", "activity[T.one] 0.4710 0.055 8.571 0.000 0.362 0.580\n", "thorax 2.7215 0.233 11.666 0.000 2.259 3.183\n", "==============================================================================\n", "Omnibus: 2.504 Durbin-Watson: 1.883\n", "Prob(Omnibus): 0.286 Jarque-Bera (JB): 2.448\n", "Skew: -0.283 Prob(JB): 0.294\n", "Kurtosis: 2.609 Cond. No. 23.5\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('np.log(longevity) ~ thorax+activity', ff).fit()\n", "lmod.summary()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.residplot(lmod.fittedvalues, lmod.resid, lowess=True)\n", "plt.xlabel(\"Fitted values\")\n", "plt.ylabel(\"Residuals\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "activity[T.isolated] 1.520824\n", "activity[T.low] 1.343643\n", "activity[T.many] 1.660571\n", "activity[T.one] 1.601589\n", "dtype: float64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(lmod.params[1:5])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: longevity R-squared: 0.325
Model: OLS Adj. R-squared: 0.302
Method: Least Squares F-statistic: 14.33
Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.41e-09
Time: 15:59:21 Log-Likelihood: -506.11
No. Observations: 124 AIC: 1022.
Df Residuals: 119 BIC: 1036.
Df Model: 4
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 38.7200 2.926 13.232 0.000 32.926 44.514
activity[T.isolated] 24.8400 4.138 6.002 0.000 16.646 33.034
activity[T.low] 18.0400 4.138 4.359 0.000 9.846 26.234
activity[T.many] 25.8217 4.181 6.175 0.000 17.542 34.101
activity[T.one] 26.0800 4.138 6.302 0.000 17.886 34.274
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Omnibus: 1.296 Durbin-Watson: 1.096
Prob(Omnibus): 0.523 Jarque-Bera (JB): 1.141
Skew: 0.034 Prob(JB): 0.565
Kurtosis: 2.535 Cond. No. 5.81


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: longevity R-squared: 0.325\n", "Model: OLS Adj. R-squared: 0.302\n", "Method: Least Squares F-statistic: 14.33\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 1.41e-09\n", "Time: 15:59:21 Log-Likelihood: -506.11\n", "No. Observations: 124 AIC: 1022.\n", "Df Residuals: 119 BIC: 1036.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------\n", "Intercept 38.7200 2.926 13.232 0.000 32.926 44.514\n", "activity[T.isolated] 24.8400 4.138 6.002 0.000 16.646 33.034\n", "activity[T.low] 18.0400 4.138 4.359 0.000 9.846 26.234\n", "activity[T.many] 25.8217 4.181 6.175 0.000 17.542 34.101\n", "activity[T.one] 26.0800 4.138 6.302 0.000 17.886 34.274\n", "==============================================================================\n", "Omnibus: 1.296 Durbin-Watson: 1.096\n", "Prob(Omnibus): 0.523 Jarque-Bera (JB): 1.141\n", "Skew: 0.034 Prob(JB): 0.565\n", "Kurtosis: 2.535 Cond. No. 5.81\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('longevity ~ activity', ff).fit()\n", "lmod.summary()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dfsum_sqmean_sqFPR(>F)
activity4.012269.4671513067.36678814.3280221.411349e-09
Residual119.025475.718333214.081667NaNNaN
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" ], "text/plain": [ " df sum_sq mean_sq F PR(>F)\n", "activity 4.0 12269.467151 3067.366788 14.328022 1.411349e-09\n", "Residual 119.0 25475.718333 214.081667 NaN NaN" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.stats.anova_lm(lmod)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: np.log(longevity) R-squared: 0.359
Model: OLS Adj. R-squared: 0.338
Method: Least Squares F-statistic: 16.69
Date: Tue, 25 Sep 2018 Prob (F-statistic): 6.96e-11
Time: 15:59:21 Log-Likelihood: -16.520
No. Observations: 124 AIC: 43.04
Df Residuals: 119 BIC: 57.14
Df Model: 4
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 3.6021 0.056 63.822 0.000 3.490 3.714
activity[T.isolated] 0.5172 0.080 6.480 0.000 0.359 0.675
activity[T.low] 0.3977 0.080 4.983 0.000 0.240 0.556
activity[T.many] 0.5412 0.081 6.711 0.000 0.381 0.701
activity[T.one] 0.5407 0.080 6.774 0.000 0.383 0.699
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 12.042 Durbin-Watson: 1.036
Prob(Omnibus): 0.002 Jarque-Bera (JB): 12.531
Skew: -0.719 Prob(JB): 0.00190
Kurtosis: 3.600 Cond. No. 5.81


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(longevity) R-squared: 0.359\n", "Model: OLS Adj. R-squared: 0.338\n", "Method: Least Squares F-statistic: 16.69\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 6.96e-11\n", "Time: 15:59:21 Log-Likelihood: -16.520\n", "No. Observations: 124 AIC: 43.04\n", "Df Residuals: 119 BIC: 57.14\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------\n", "Intercept 3.6021 0.056 63.822 0.000 3.490 3.714\n", "activity[T.isolated] 0.5172 0.080 6.480 0.000 0.359 0.675\n", "activity[T.low] 0.3977 0.080 4.983 0.000 0.240 0.556\n", "activity[T.many] 0.5412 0.081 6.711 0.000 0.381 0.701\n", "activity[T.one] 0.5407 0.080 6.774 0.000 0.383 0.699\n", "==============================================================================\n", "Omnibus: 12.042 Durbin-Watson: 1.036\n", "Prob(Omnibus): 0.002 Jarque-Bera (JB): 12.531\n", "Skew: -0.719 Prob(JB): 0.00190\n", "Kurtosis: 3.600 Cond. No. 5.81\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('np.log(longevity) ~ activity', ff).fit()\n", "lmod.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Factor coding" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0. 0. 0.]\n", " [1. 0. 0.]\n", " [0. 1. 0.]\n", " [0. 0. 1.]]\n" ] } ], "source": [ "from patsy.contrasts import Treatment\n", "levels = [1,2,3,4]\n", "contrast = Treatment(reference=0).code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0. 0.]\n", " [ 0. 1. 0.]\n", " [ 0. 0. 1.]\n", " [-1. -1. -1.]]\n" ] } ], "source": [ "from patsy.contrasts import Sum\n", "contrast = Sum().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-1. -1. -1.]\n", " [ 1. -1. -1.]\n", " [ 0. 2. -1.]\n", " [ 0. 0. 3.]]\n" ] } ], "source": [ "from patsy.contrasts import Helmert\n", "contrast = Helmert().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: ptsd R-squared: 0.519
Model: OLS Adj. R-squared: 0.513
Method: Least Squares F-statistic: 79.90
Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13
Time: 15:59:21 Log-Likelihood: -201.44
No. Observations: 76 AIC: 406.9
Df Residuals: 74 BIC: 411.5
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 8.3185 0.405 20.526 0.000 7.511 9.126
C(csa, Sum)[S.Abused] 3.6226 0.405 8.939 0.000 2.815 4.430
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 1.200 Durbin-Watson: 1.830
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206
Skew: -0.198 Prob(JB): 0.547
Kurtosis: 2.527 Cond. No. 1.20


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: ptsd R-squared: 0.519\n", "Model: OLS Adj. R-squared: 0.513\n", "Method: Least Squares F-statistic: 79.90\n", "Date: Tue, 25 Sep 2018 Prob (F-statistic): 2.17e-13\n", "Time: 15:59:21 Log-Likelihood: -201.44\n", "No. Observations: 76 AIC: 406.9\n", "Df Residuals: 74 BIC: 411.5\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "=========================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------------\n", "Intercept 8.3185 0.405 20.526 0.000 7.511 9.126\n", "C(csa, Sum)[S.Abused] 3.6226 0.405 8.939 0.000 2.815 4.430\n", "==============================================================================\n", "Omnibus: 1.200 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.206\n", "Skew: -0.198 Prob(JB): 0.547\n", "Kurtosis: 2.527 Cond. No. 1.20\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lmod = smf.ols('ptsd ~ C(csa,Sum)', sexab).fit()\n", "lmod.summary()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "application/json": { "Software versions": [ { "module": "Python", "version": "3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]" }, { "module": "IPython", "version": "6.5.0" }, { "module": "OS", "version": "Darwin 17.7.0 x86_64 i386 64bit" }, { "module": "pandas", "version": "0.23.4" }, { "module": "numpy", "version": "1.15.1" }, { "module": "matplotlib", "version": "2.2.3" }, { "module": "seaborn", "version": "0.9.0" }, { "module": "scipy", "version": "1.1.0" }, { "module": "patsy", "version": "0.5.0" }, { "module": "statsmodels", "version": "0.9.0" } ] }, "text/html": [ "
SoftwareVersion
Python3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]
IPython6.5.0
OSDarwin 17.7.0 x86_64 i386 64bit
pandas0.23.4
numpy1.15.1
matplotlib2.2.3
seaborn0.9.0
scipy1.1.0
patsy0.5.0
statsmodels0.9.0
Tue Sep 25 15:59:21 2018 BST
" ], "text/latex": [ "\\begin{tabular}{|l|l|}\\hline\n", "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", "Python & 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE\\_401/final)] \\\\ \\hline\n", "IPython & 6.5.0 \\\\ \\hline\n", "OS & Darwin 17.7.0 x86\\_64 i386 64bit \\\\ \\hline\n", "pandas & 0.23.4 \\\\ \\hline\n", "numpy & 1.15.1 \\\\ \\hline\n", "matplotlib & 2.2.3 \\\\ \\hline\n", "seaborn & 0.9.0 \\\\ \\hline\n", "scipy & 1.1.0 \\\\ \\hline\n", "patsy & 0.5.0 \\\\ \\hline\n", "statsmodels & 0.9.0 \\\\ \\hline\n", "\\hline \\multicolumn{2}{|l|}{Tue Sep 25 15:59:21 2018 BST} \\\\ \\hline\n", "\\end{tabular}\n" ], "text/plain": [ "Software versions\n", "Python 3.7.0 64bit [Clang 4.0.1 (tags/RELEASE_401/final)]\n", "IPython 6.5.0\n", "OS Darwin 17.7.0 x86_64 i386 64bit\n", "pandas 0.23.4\n", "numpy 1.15.1\n", "matplotlib 2.2.3\n", "seaborn 0.9.0\n", "scipy 1.1.0\n", "patsy 0.5.0\n", "statsmodels 0.9.0\n", "Tue Sep 25 15:59:21 2018 BST" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%load_ext version_information\n", "%version_information pandas, numpy, matplotlib, seaborn, scipy, patsy, statsmodels" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }