cbpp_PB {lme4} | R Documentation |
cbpp
data
Examples of parametric bootstrap distributions
computed from models fitted to the
cbpp
data set
cbpp_PB
Contains a vector of 500 computed deviances under the (simulated) null hypothesis
simulate-mer
for examples of
what to do with the parametric boostrap results
## PB test of significance of main effect of period gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) gm0 <- update(gm1, . ~. -period) ## generic parametric bootstrapping function; return a single simulated deviance ## difference between full (`m1') and reduced (`m0') models under the ## null hypothesis that the reduced model is the true model pboot <- function(m0,m1) { s <- simulate(m0) L0 <- logLik(refit(m0,s)) L1 <- logLik(refit(m1,s)) 2*(L1-L0) } obsdev <- c(2*(logLik(gm1)-logLik(gm0))) ## Not run: ## parametric bootstrap test of significance of correlation between ## random effects of `(Intercept)` and Days ## Timing approx. 240 secs on a 2.66 GHz Intel Core Duo laptop set.seed(1001) cbpp_PB <- replicate(500,pboot(gm0,gm1)) ## End(Not run)