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modmarg

Calculate predicted levels and marginal effects using the delta method to calculate standard errors. This is an R-based version of Stata’s ‘margins’ command.

Features:

• Calculate predictive levels and margins for glm and ivreg objects (more models to be added - PRs welcome) using closed-form derivatives

• Add custom variance-covariance matrices to all calculations to add, e.g., clustered or robust standard errors (for more information on replicating Stata analyses, see here)

• Frequency weights are incorporated into margins and effects

Usage

To install this package from CRAN, please run

install.packages('modmarg')

To install the development version of this package, please run

devtools::install_github('anniejw6/modmarg', build_vignettes = TRUE)

Here is an example of estimating predicted levels and effects using the iris dataset:

data(iris)

mod <- glm(Sepal.Length ~ Sepal.Width + Species, 
           data = iris, family = 'gaussian')
           
# Predicted Levels
modmarg::marg(mod, var_interest = 'Species', type = 'levels')

# Predicted Effects
modmarg::marg(mod, var_interest = 'Species', type = 'effects')

There are two vignettes included:

vignette('usage', package = 'modmarg')
vignette('delta-method', package = 'modmarg')

More Reading on the Delta Method

• Delta Method: This is from the appendix the book guide to the MARK program, developed by Gary White.

• How can I estimate the standard error of transformed regression parameters in R using the delta method? from UCLA.

• The Delta method to estimate standard errors from a non-linear transformation from Econometrics by Simulation.

• A FAQ on Margins from the Stata Blog

• Delta Method on Wikipedia

• What is the intuition behind the sandwich estimator? from StackExchange

• Least Squares Optimization by Harald E. Krogstad

• The robust sandwich variance estimator for linear regression (theory) by Jonathan Bartlett

• Using Stata’s Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects by Richard Williams

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.