mem: Marginal effects at means

In andreizhirnov/DAME: DAME: data-driven interpretation of interactive nonlinear models

Marginal effects at means

Description

mem computes the marginal effects of variable x at the specified values of at variables and the mean values of all the other variables (including x).

Usage

mem(
  x,
  model = NULL,
  data = NULL,
  formula = NULL,
  link = NULL,
  coefficients = NULL,
  vcov = NULL,
  discrete = FALSE,
  discrete_step = 1,
  at = NULL,
  mc = FALSE,
  pct = c(lb = 2.5, ub = 97.5),
  iter = 1000,
  weights = NULL
)

Arguments

x	a character string representing the name of the main variable of interest. Marginal effects will be computed for this variable.
model	fitted model object. The package works best with GLM objects and will extract the formula, dataset, family, coefficients, and the QR components of the design matrix if arguments formula, data, link, coefficients, and/or vcov are not explicitly specified.
data	the dataset to be used to compute marginal effects (if not specified, it is extracted from the fitted model object).
formula	the formula used in estimation (if not specified, it is extracted from the fitted model object).
link	the name of the link function used in estimation (if not specified, it is extracted from the fitted model object).
coefficients	the named vector of coefficients produced during the estimation (if not specified, it is extracted from the fitted model object).
vcov	the variance-covariance matrix to be used for computing standard errors (if not specified, it is extracted from the fitted model object).
discrete	A logical variable. If TRUE, the function will compute the effect of a discrete change in x. If FALSE, the function will compute the partial derivative of x.
discrete_step	The size of a discrete change in x used in computations (used only if discrete=TRUE).
at	an optional named list of values of independent variables. These variables will be set to these value before computations. The remaining numeric variables (except x and over) will be set to their means. The remaining factor variables will be set to their modes.
mc	logical. If TRUE, the standard errors and confidence intervals will be computed using simulations. If FALSE (default), the delta method will be used.
pct	a named numeric vector with the sampling quantiles to be output with the DAME estimates (the names are used as the new variable names). Default = c(lb=2.5,ub=97.5).
iter	the number of interations used in Monte-Carlo simulations. Default = 1,000.
weights	an optional vector of sampling weights.

Value

mem returns a data frame with the estimates of the marginal effects at means, along with standard errors, confidence intervals, and the used values of the independent variables. All quantitative variable not included in at are set to their means, and all qualitative variables (except those listed in at) are converted to factors and set to their modes.

Examples

##poisson regression with 2 variables and an interaction between them
#fit the regression first
data <- data.frame(y = rpois(10000, 10), x2 = rpois(10000, 5),
x1 = rpois(10000, 3), w=c("a","b","c","d"))
y <- glm(y ~ x1*x2 + w, data = data, family = "poisson")
#compute marginal effects at means
mem(model = y, x = "x1",
     at=list(x2=seq(min(data$x2), max(data$x2), length.out=5)))
## Not run: 
## logit
m <- glm(any_dispute ~ flows.ln*polity2 + gdp_pc, data=strikes, family="binomial")
summary(m)
## marginal effects at means with a robust (heteroscedasticity-consistent)
variance-covariance matrix
library(sandwich)
mem(model=m, x="flows.ln", vcov=vcovHC(m), at=list(polity2=c(-10,0,10)))

## End(Not run)

andreizhirnov/DAME documentation built on Nov. 21, 2022, 5:55 p.m.