Note

This document is based heavily on this excellent resource from UCLA.

A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses.

In fact, the dummy coding is not technically a contrast coding. This is because the dummy variables add to one and are not functionally independent of the model’s intercept. On the other hand, a set of *contrasts* for a categorical variable with `k`

levels is a set of `k-1`

functionally independent linear combinations of the factor level means that are also independent of the sum of the dummy variables. The dummy coding isn’t wrong *per se*. It captures all of the coefficients, but it complicates matters when the model assumes independence of the coefficients such as in ANOVA. Linear regression models do not assume independence of the coefficients and thus dummy coding is often the only coding that is taught in this context.

To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS. First let’s load the data.

In [1]: import pandas In [2]: url = 'http://www.ats.ucla.edu/stat/data/hsb2.csv' In [3]: hsb2 = pandas.read_table(url, delimiter=",")

It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian)).

Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be

In [4]: from patsy.contrasts import Treatment In [5]: levels = [1,2,3,4] In [6]: contrast = Treatment(reference=0).code_without_intercept(levels) In [7]: print(contrast.matrix) [[ 0. 0. 0.] [ 1. 0. 0.] [ 0. 1. 0.] [ 0. 0. 1.]]

Here we used `reference=0`

, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let’s look at how this would encode the `race`

variable.

In [8]: contrast.matrix[hsb2.race-1, :][:20] Out[8]: array([[ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 1., 0.], [ 0., 0., 0.], [ 0., 0., 1.], [ 0., 1., 0.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 1., 0.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.], [ 0., 0., 1.]])

This is a bit of a trick, as the `race`

category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this won’t work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above

In [9]: from statsmodels.formula.api import ols In [10]: mod = ols("write ~ C(race, Treatment)", data=hsb2) In [11]: res = mod.fit() In [12]: print(res.summary()) OLS Regression Results ============================================================================== Dep. Variable: write R-squared: 0.107 Model: OLS Adj. R-squared: 0.093 Method: Least Squares F-statistic: 7.833 Date: Tue, 28 Feb 2017 Prob (F-statistic): 5.78e-05 Time: 21:35:59 Log-Likelihood: -721.77 No. Observations: 200 AIC: 1452. Df Residuals: 196 BIC: 1465. Df Model: 3 Covariance Type: nonrobust =========================================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------------------- Intercept 46.4583 1.842 25.218 0.000 42.825 50.091 C(race, Treatment)[T.2] 11.5417 3.286 3.512 0.001 5.061 18.022 C(race, Treatment)[T.3] 1.7417 2.732 0.637 0.525 -3.647 7.131 C(race, Treatment)[T.4] 7.5968 1.989 3.820 0.000 3.675 11.519 ============================================================================== Omnibus: 10.487 Durbin-Watson: 1.779 Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031 Skew: -0.551 Prob(JB): 0.00402 Kurtosis: 2.670 Cond. No. 8.25 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this.

Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. See User-Defined Coding for how to implement the Simple contrast.

In [13]: contrast = Simple().code_without_intercept(levels) In [14]: print(contrast.matrix) [[-0.25 -0.25 -0.25] [ 0.75 -0.25 -0.25] [-0.25 0.75 -0.25] [-0.25 -0.25 0.75]] In [15]: mod = ols("write ~ C(race, Simple)", data=hsb2) In [16]: res = mod.fit() In [17]: print(res.summary()) OLS Regression Results ============================================================================== Dep. Variable: write R-squared: 0.107 Model: OLS Adj. R-squared: 0.093 Method: Least Squares F-statistic: 7.833 Date: Tue, 28 Feb 2017 Prob (F-statistic): 5.78e-05 Time: 21:35:59 Log-Likelihood: -721.77 No. Observations: 200 AIC: 1452. Df Residuals: 196 BIC: 1465. Df Model: 3 Covariance Type: nonrobust =========================================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------------------- Intercept 51.6784 0.982 52.619 0.000 49.741 53.615 C(race, Simple)[Simp.1] 11.5417 3.286 3.512 0.001 5.061 18.022 C(race, Simple)[Simp.2] 1.7417 2.732 0.637 0.525 -3.647 7.131 C(race, Simple)[Simp.3] 7.5968 1.989 3.820 0.000 3.675 11.519 ============================================================================== Omnibus: 10.487 Durbin-Watson: 1.779 Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031 Skew: -0.551 Prob(JB): 0.00402 Kurtosis: 2.670 Cond. No. 7.03 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others.

In [18]: from patsy.contrasts import Sum In [19]: contrast = Sum().code_without_intercept(levels) In [20]: print(contrast.matrix) [[ 1. 0. 0.] [ 0. 1. 0.] [ 0. 0. 1.] [-1. -1. -1.]] In [21]: mod = ols("write ~ C(race, Sum)", data=hsb2) In [22]: res = mod.fit() In [23]: print(res.summary()) OLS Regression Results ============================================================================== Dep. Variable: write R-squared: 0.107 Model: OLS Adj. R-squared: 0.093 Method: Least Squares F-statistic: 7.833 Date: Tue, 28 Feb 2017 Prob (F-statistic): 5.78e-05 Time: 21:36:00 Log-Likelihood: -721.77 No. Observations: 200 AIC: 1452. Df Residuals: 196 BIC: 1465. Df Model: 3 Covariance Type: nonrobust ===================================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------------- Intercept 51.6784 0.982 52.619 0.000 49.741 53.615 C(race, Sum)[S.1] -5.2200 1.631 -3.200 0.002 -8.437 -2.003 C(race, Sum)[S.2] 6.3216 2.160 2.926 0.004 2.061 10.582 C(race, Sum)[S.3] -3.4784 1.732 -2.008 0.046 -6.895 -0.062 ============================================================================== Omnibus: 10.487 Durbin-Watson: 1.779 Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031 Skew: -0.551 Prob(JB): 0.00402 Kurtosis: 2.670 Cond. No. 6.72 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

This correspons to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level.

In [24]: hsb2.groupby('race')['write'].mean().mean() Out[24]: 51.678376436781619

In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable.

In [25]: from patsy.contrasts import Diff In [26]: contrast = Diff().code_without_intercept(levels) In [27]: print(contrast.matrix) [[-0.75 -0.5 -0.25] [ 0.25 -0.5 -0.25] [ 0.25 0.5 -0.25] [ 0.25 0.5 0.75]] In [28]: mod = ols("write ~ C(race, Diff)", data=hsb2) In [29]: res = mod.fit() In [30]: print(res.summary()) OLS Regression Results ============================================================================== Dep. Variable: write R-squared: 0.107 Model: OLS Adj. R-squared: 0.093 Method: Least Squares F-statistic: 7.833 Date: Tue, 28 Feb 2017 Prob (F-statistic): 5.78e-05 Time: 21:36:00 Log-Likelihood: -721.77 No. Observations: 200 AIC: 1452. Df Residuals: 196 BIC: 1465. Df Model: 3 Covariance Type: nonrobust ====================================================================================== coef std err t P>|t| [0.025 0.975] -------------------------------------------------------------------------------------- Intercept 51.6784 0.982 52.619 0.000 49.741 53.615 C(race, Diff)[D.1] 11.5417 3.286 3.512 0.001 5.061 18.022 C(race, Diff)[D.2] -9.8000 3.388 -2.893 0.004 -16.481 -3.119 C(race, Diff)[D.3] 5.8552 2.153 2.720 0.007 1.610 10.101 ============================================================================== Omnibus: 10.487 Durbin-Watson: 1.779 Prob(Omnibus): 0.005 Jarque-Bera (JB): 11.031 Skew: -0.551 Prob(JB): 0.00402 Kurtosis: 2.670 Cond. No. 8.30 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

For example, here the coefficient on level 1 is the mean of `write`

at level 2 compared with the mean at level 1. Ie.,

In [31]: res.params["C(race, Diff)[D.1]"] Out[31]: 11.541666666666659 In [32]: hsb2.groupby('race').mean()["write"][2] - \ ....: hsb2.groupby('race').mean()["write"][1] ....:

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© 2006–2008 Scipy Developers

© 2006 Jonathan E. Taylor

Licensed under the 3-clause BSD License.

http://www.statsmodels.org/stable/contrasts.html