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)