I am running a glmer model and I want to determine the total variance. My data is for survival and it is coded as 0 and 1, where 1 represents that the individual survived and 0 represents that the individual died. My data represents offspring from a full factorial cross where some individuals are full sibs or half sibs.
When running a glmer model, and there is no residual variance in the summary output. I have read that the residual variance should be (π^2)/3 for generalized linear mixed models with binomial data and logit link function (Nakagawa, S., Schielzeth, H. 2010. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol. Rev. 85:935-956.).
Is this true? Or is there a different way to calculate the residual variance for glmer?
Here is my model and output:
model6 = glmer(X09.Nov~(1|Dam)+(1|Sire)+(1|Sire:Dam), family=binomial, data=data) summary(model6) Generalized linear mixed model fit by maximum likelihood (Laplace Approximation [glmerMod] Family: binomial ( logit ) Formula: X09.Nov ~ (1 | Dam) + (1 | Sire) + (1 | Sire:Dam) Data: data AIC BIC logLik deviance df.resid 1274.4 1295.3 -633.2 1266.4 1375 Scaled residuals: Min 1Q Median 3Q Max -3.2747 0.3366 0.3931 0.4664 1.1090 Random effects: Groups Name Variance Std.Dev. Sire:Dam (Intercept) 3.853e-01 6.207e-01 Sire (Intercept) 4.181e-02 2.045e-01 Dam (Intercept) 6.036e-09 7.769e-05 Number of obs: 1379, groups: Sire:Dam, 49; Sire, 7; Dam, 7 Fixed effects: Estimate Std. Error z value Pr (Intercept) 1.6456 0.1419 11.6 <2e-16 *