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 derivativesAdd 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

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')
```

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

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

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