# [R-sig-ME] negative binomial ICC for the GLMM

Birnbaum, Lisa ||@@@b|rnb@um @end|ng |rom |@u@de
Wed Jan 23 08:27:32 CET 2019

```Dear Timothy,

I wanna calculate a negative binomial ICC for the null model with Rdistanc as depedent variable and schoolid as cluster variable. In the last step I get an error message. I am not sure if I did it right with the numerator. Can you please help me?

This is my R-code:

#Specify the model first

model = glmer.nb(formula = Rdistanc ~ 1 + (1 | schoolid), data = dat)

# Then execute this code for ICC calculation for negative binomial.
#There is nothing you need to change to the code, it's a function independent of the data.
#################################
ICC.NB <- function(model, numerator){
require(lme4)
mout <- data.frame(VarCorr(model)) # random intercept model variances
sigma_a2 <- sum(mout[mout\$grp %in% numerator, "vcov"]) # random effect(s) in numerator

sigma_2 <- sum(mout["vcov"]) # sum of random effects variance in denominator
beta <- as.numeric(fixef(model)["(Intercept)"]) # fixed effect intercept
r <- getME(object = model, "glmer.nb.theta") # theta
icc <- (exp(sigma_a2) - 1) / ((exp(sigma_2) - 1) + (exp(sigma_2) /
r) + (exp(-beta) - (sigma_2 / 2)))
return(icc)
}
##################################
# Lastly, run
ICC.NB (glmer.nb(formula = dat\$Rdistanc ~ 1 + (1 | dat\$schoolid), data = dat, numerator = dat\$schoolid))

I really would appreciate your help.
Best regards,
Lisa

--
Lisa Birnbaum, M.A.
Friedrich-Alexander-Universit�t Erlangen-N�rnberg
Lehrstuhl f�r Empirische Bildungsforschung
Regensburger Str. 160
90478 N�rnberg
Mail: lisa.birnbaum using fau.de

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