predicted probability plot with robust errors for logit model - r

Question

I'm trying to make a predicted probability plot for a logit model, using clustered robust standard errors. Supposedly the margins package should let you do this, using cplot(), but there seems to be a bug, such that cplot() doesn't recognize the optional vcov input. Below is a minimum working example. Does anyone know how to fix the bug or do this another way?
require("margins")
require("sandwich")
##Generating random numbers
set.seed(10)
y<-factor(rbinom(n=1000,size=1,prob=.5))
x <- rnorm(n=1000, mean=100,sd=1)
z<- rbinom(n=1000,size=3,prob=.5)
#creating a "dataset"
dta<-data.frame(x,y,z)
##Basic logit model
model <-glm(y~x,family="binomial"(link="logit"),data=dta)
##Creating variance-covariance matrix, clustered by z
vcov <- vcovCL(model, cluster=z)
##Making a plot
cplot(model,"x",vcov=vcov,what="prediction")
#can see below that vcov has no effect (if not obvious from plot)
print(cplot(model,"x",vcov=vcov,what="prediction",draw=FALSE))
print(cplot(model,"x",what="prediction",draw=FALSE))

Answer 1

You could use the following code:
# Predict values
pred.dta <- ggeffects::ggpredict(
model=model,
terms="x [all]",
vcov.fun="vcovCL",
vcov.type="HC1",
vcov.args=list(cluster=z)
)
# Plot predictions
ggplot2::ggplot(data=pred.dta,
ggplot2::aes(x=x, y=predicted))+
ggplot2::geom_line()+
ggplot2::geom_errorbar(ggplot2::aes(ymin=conf.low, ymax=conf.high), width=.1)
For comparison, this is the same code but without the clustered errors:
# Predict values
pred.dta <- ggeffects::ggpredict(
model=model,
terms="x [all]" )
# Plot predictions
ggplot2::ggplot(data=pred.dta,
ggplot2::aes(x=x, y=predicted))+
ggplot2::geom_line()+
ggplot2::geom_errorbar(ggplot2::aes(ymin=conf.low, ymax=conf.high), width=.1)