How to compute the standard errors of a logistic regression's coefficients

Question

I am using Python's scikit-learn to train and test a logistic regression.

scikit-learn returns the regression's coefficients of the independent variables, but it does not provide the coefficients' standard errors. I need these standard errors to compute a Wald statistic for each coefficient and, in turn, compare these coefficients to each other.

I have found one description of how to compute standard errors for the coefficients of a logistic regression (here), but it is somewhat difficult to follow.

If you happen to know of a simple, succint explanation of how to compute these standard errors and/or can provide me with one, I'd really appreciate it! I don't mean specific code (though please feel free to post any code that might be helpful), but rather an algorithmic explanation of the steps involved.

Answer 1

Does your software give you a parameter covariance (or variance-covariance) matrix? If so, the standard errors are the square root of the diagonal of that matrix. You probably want to consult a textbook (or google for university lecture notes) for how to get the $V_\beta$ matrix for linear and generalized linear models.

Answer 2

The standard errors of the model coefficients are the square roots of the diagonal entries of the covariance matrix. Consider the following:

• Design matrix:

$\textbf{X = }\begin{bmatrix} 1 & x_{1,1} & \ldots & x_{1,p} \\ 1 & x_{2,1} & \ldots & x_{2,p} \\ \vdots & \vdots & \ddots & \vdots \\ 1 & x_{n,1} & \ldots & x_{n,p} \end{bmatrix}$ , where $x_{i,j}$ is the value of the $j$th predictor for the $i$th observations.

(NOTE: This assumes a model with an intercept.)

• $\textbf{V = } \begin{bmatrix} \hat{\pi}_{1}(1 - \hat{\pi}_{1}) & 0 & \ldots & 0 \\ 0 & \hat{\pi}_{2}(1 - \hat{\pi}_{2}) & \ldots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \ldots & \hat{\pi}_{n}(1 - \hat{\pi}_{n}) \end{bmatrix}$ , where $\hat{\pi}_{i}$ represents the predicted probability of class membership for observation $i$.

The covariance matrix can be written as:

$\textbf{(X}^{T}\textbf{V}\textbf{X)}^{-1}$

This can be implemented with the following code:

import numpy as np
from sklearn import linear_model

# Initiate logistic regression object
logit = linear_model.LogisticRegression()

# Fit model. Let X_train = matrix of predictors, y_train = matrix of variable.
# NOTE: Do not include a column for the intercept when fitting the model.
resLogit = logit.fit(X_train, y_train)

# Calculate matrix of predicted class probabilities.
# Check resLogit.classes_ to make sure that sklearn ordered your classes as expected
predProbs = resLogit.predict_proba(X_train)

# Design matrix -- add column of 1's at the beginning of your X_train matrix
X_design = np.hstack([np.ones((X_train.shape[0], 1)), X_train])

# Initiate matrix of 0's, fill diagonal with each predicted observation's variance
V = np.diagflat(np.product(predProbs, axis=1))

# Covariance matrix
# Note that the @-operater does matrix multiplication in Python 3.5+, so if you're running
# Python 3.5+, you can replace the covLogit-line below with the more readable:
# covLogit = np.linalg.inv(X_design.T @ V @ X_design)
covLogit = np.linalg.inv(np.dot(np.dot(X_design.T, V), X_design))
print("Covariance matrix: ", covLogit)

# Standard errors
print("Standard errors: ", np.sqrt(np.diag(covLogit)))

# Wald statistic (coefficient / s.e.) ^ 2
logitParams = np.insert(resLogit.coef_, 0, resLogit.intercept_)
print("Wald statistics: ", (logitParams / np.sqrt(np.diag(covLogit))) ** 2)

All that being said, statsmodels will probably be a better package to use if you want access to a LOT of "out-the-box" diagnostics.

Answer 3

If you're interested in doing inference, then you'll probably want to have a look at statsmodels. Standard errors and common statistical tests are available. Here's a logistic regression example.

Answer 4

Building upon the fantastic work of @j_sack I have two impulses to build on his code:

1. Since predict_proba is giving you the probability for n-classes, the result is an nD-array and that is why one should(?) specify the class of interest, e.g.:

predProbs[:,0]

(check class of interest with resLogit.classes_)

2. The diagonal matrix should not include the raw-predicted-probability but $w_{ii}=\pi_i(1-\pi_i) $ e.g:

   V = np.diagflat(np.product(predProbs[:,0] * (1-predProbs[:,0]), axis=1))