get_margins: Get Marginal Probabilities for a Logistic Regression Model

In jameshenegan/dmsepmlr: Delta Method Standard Errors for various quantities related to the Predictive Margins of a Logistic Regression Model

Description

For a logistic regression model that includes one main effect and zero or more adjustors, obtain marginal probalities for the outcome variable with respect to the main effect. This reproduces the behavior of running margins x after fitting the logistic regression model in Stata.

Usage

get_margins(model_data, betas, sigma, main_effect, over)

Arguments

model_data	The data used to fit the Logistic Regression model
betas	The beta coefficients obtained from fitting the Logistic Regression model
sigma	The variance-covariance matrix obtained from fitting the Logistic Regression model
main_effect	The name of the variable corresponding to the main effect in your model.
over	A Boolean variable. If TRUE, compute the equivalent of the Stata command margins, over(var_name). If FALSE, compute the equivalent of margins var_name.

Value

The marginal probabilities, their standard errors, and their variance-covariance matrix.

jameshenegan/dmsepmlr documentation built on Jan. 1, 2021, 4:27 a.m.