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.
start
starting values for the parameters in the glm model.
control
see glm.control.
Details
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 glm 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.
See Also
poissonirr, glm
Examples
# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)
y = rnegbin(n, mu = exp(1 + 0.5 * x), theta = 0.5)
data = data.frame(y,x)
poissonmfx(formula=y~x,data=data)