Marginal effects for a beta regression.
This function estimates a beta regression model and calculates the corresponding marginal effects.
betamfx(formula, data, atmean = TRUE, robust = FALSE,
clustervar1 = NULL, clustervar2 = NULL,
control = betareg.control(), link.phi = NULL, type = "ML")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 |
robust |
if |
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 |
link.phi |
as in the |
type |
as in the |
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.
mfxest |
a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values. |
fit |
the fitted |
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. |
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.
# 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)
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