I need to extract the posterior estimates and intervals for a random effect from my model.
For illustrative purposes, a similar dataset to the one I am using would be the ChickWeight dataset in base R.
The way I extract the posterior estimates and intervals for my fixed effects is like so:
#load package
library(lme4)
#model
m.surv<-lmer(weight ~ Time + Diet + (1|Chick), data=ChickWeight)
#load packages
library(MCMCglmm)
library(arm)
#set up for fixed effects
sm.surv<-sim(m.surv)
smfixef.surv=sm.surv@fixef
smfixef.surv=as.mcmc(smfixef.surv)
#which gives
> posterior.mode(smfixef.surv)
(Intercept) Time Diet2 ...
8.5963329 8.7034260 5.1220436 ...
> HPDinterval(smfixef.surv)
lower upper
(Intercept) -0.90309142 21.3617805
Time 8.42279728 9.0306337
Diet2 -6.84371527 35.1745980
...
attr(,"Probability")
[1] 0.95
>
When I try this for the random effect (Chick), I get the following error at the second line of code:
smranef.surv=sm.surv@ranef
smranef.surv=as.mcmc(smranef.surv)
Error in mcmc.list(x) : Arguments must be mcmc objects
Any suggestions on how I can modify my code to extract these values for the random effect?
Note for other users: if the model would have been a MCMCglmm model, the posterior mode values for the MCMC output for the random effects can be extracted like so:
posterior.mode(sm.surv$VCV[,1])
HPDinterval(sm.surv$VCV[,1])
To extract the estimate and 95% CI for your random effects, you use the following code:
sm.surv <-sim(m.surv)
#between Chick variance
bChick <-sm@ranef$Chick[,,1]
bvar<-as.vector(apply(bChick, 1, var)) #between ind variance posterior distribution
bvar<-as.mcmc(bvar)
posterior.mode(bvar) #mode of the distribution
HPDinterval(bvar)
This will then give you:
> posterior.mode(bvar)
var1
501.24353
> HPDinterval(bvar)
lower upper
var1 412.36042 630.201
attr(,"Probability")
[1] 0.95
This means that the estimate is 501 and the lower 95% interval was 412 and the upper 95% interval was 630.