Binomial GLM in R: Is there any overdispersion test, like AER package?

Question

I have (mydata) as example:

mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
## view the first few rows of the data
head(mydata)

mydata$rank <- factor(mydata$rank)
mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial")

summary(mylogit)

I try to see if I have model overdispersion:

458.52/394 #[1] 1.163756 Almost 1!!!

But I not sure ... there is the package AER that makes the overdispersion test for Poisson and Binomial Negative and for Binomial there is any option?

Thanks in advance!

Answer 1

By Ben Bolker and others (https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#overdispersion)

overdisp_fun <- function(model) {
    rdf <- df.residual(model)
    rp <- residuals(model,type="pearson")
    Pearson.chisq <- sum(rp^2)
    prat <- Pearson.chisq/rdf
    pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
    c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
}

overdisp_fun (mylogit)

#      chisq       ratio         rdf           p 
#397.4901989   1.0088584 394.0000000   0.4412888

Not overdispersion model!!!