# Is it necessary to report standard errors with marginal effects?

I've run a probit regression in R with a random effect and can find no way to get the marginal effects with s.e. and p values. I have therefore tried to calculate the marginal effects 'by hand' by using the probit scalars and regression coefficients. However, I do not know how to get p values or standard errors and as far as I have found there is no easy way to do this for a mixed effects probit regression.

My model m1 is

 m1<-glmer(Success~Name.Origin+(1|Job.ID),family=binomial(link="probit"))

• What do you mean by marginal effects? The estimated probits for each Name.Origin value? Or something else? (In fact, is Name.Origin a factor or a covariate?) – Russ Lenth Aug 19 '15 at 0:03
• @rvl Name.Origin is a factor so is basically a dummy variable. No, I calculated marginal effects by multiplying ProbitScalar by the regression coefficients as I do not know (and cannot find) any other way to calculate marginal effects for a glmer which also show standard errors and p values. I'm not sure it is even possible unless you write your own code/function from scratch which I have no idea how to do....do you know of any way? – Lola2000 Aug 19 '15 at 0:26

Well, if I understand it correctly, you just want the prediction from the model at each level of Name.Origin. The predict function for merMod objects should in principle work, but I don't see an argument in it to obtain SEs.
library("lsmeans")

• The one with type="response" gives the estimated probabilities. The pairs one does tests comparing the different factor levels. It appears you are flying somewhat blind here, and I suggest you find a stat consultant (say at a stat dept at a nearby university) and get them to sit down with you and explain this stuff more carefully than can be done here. – Russ Lenth Aug 19 '15 at 21:57