betamfx: Marginal effects for a beta regression.

In mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs

Marginal effects for a beta regression.

Description

This function estimates a beta regression model and calculates the corresponding marginal effects.

Usage

betamfx(formula, data, atmean = TRUE, robust = FALSE, 
        clustervar1 = NULL, clustervar2 = NULL, 
        control = betareg.control(), link.phi = NULL, type = "ML")

Arguments

formula	an object of class “formula” (or one that can be coerced to that class).
data	the data frame containing these data. This argument must be used.
atmean	default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects.
robust	if TRUE the function reports White/robust standard errors.
clustervar1	a character value naming the first cluster on which to adjust the standard errors.
clustervar2	a character value naming the second cluster on which to adjust the standard errors for two-way clustering.
control	a list of control arguments specified via betareg.control.
link.phi	as in the betareg function.
type	as in the betareg function.

Details

The underlying link function in the mean model (mu) is “logit”. If both robust=TRUE and !is.null(clustervar1) the function overrides the robust command and computes clustered standard errors.

Value

mfxest	a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values.
fit	the fitted betareg object.
dcvar	a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable.
call	the matched call.

References

Francisco Cribari-Neto, Achim Zeileis (2010). Beta Regression in R. Journal of Statistical Software 34(2), 1-24.

Bettina Gruen, Ioannis Kosmidis, Achim Zeileis (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), 1-25.

See Also

betaor, betareg

Examples

# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)

# beta outcome
y = rbeta(n, shape1 = plogis(1 + 0.5 * x), shape2 = (abs(0.2*x)))
# use Smithson and Verkuilen correction
y = (y*(n-1)+0.5)/n

data = data.frame(y,x)
betamfx(y~x|x, data=data)

mfx documentation built on June 8, 2025, 10:43 a.m.