PBrefdist {pbkrtest} | R Documentation |
Calculate reference distribution of likelihood ratio statistic in mixed effects models using parametric bootstrap
PBrefdist(largeModel, smallModel, nsim = 1000, seed=NULL, cl = NULL, details = 0)
largeModel |
A linear mixed effects model as fitted with the |
smallModel |
A linear mixed effects model as fitted with the |
nsim |
The number of simulations to form the reference distribution. |
seed |
Seed for the random number generation. |
cl |
A vector identifying a cluster; used for calculating the reference distribution using several cores. See examples below. |
details |
The amount of output produced. Mainly relevant for debugging purposes. |
The model object
must be fitted with maximum likelihood (i.e. with
REML=FALSE
). If the object is fitted with restricted maximum
likelihood (i.e. with
REML=TRUE
) then the model is refitted with REML=FALSE
before the p-values are calculated. Put differently, the user needs
not worry about this issue.
A numeric vector
Soren Hojsgaard sorenh@math.aau.dk
Ulrich Halekoh, Søren Højsgaard (2014)., A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest., Journal of Statistical Software, 58(10), 1-30., http://www.jstatsoft.org/v59/i09/
data(beets) head(beets) beet0<-lmer(sugpct~block+sow+harvest+(1|block:harvest), data=beets, REML=FALSE) beet_no.harv <- update(beet0, .~.-harvest) rr <- PBrefdist(beet0, beet_no.harv, nsim=20) rr ## Note clearly many more than 10 simulations must be made in practice. ## Computations can be made in parallel using several processors: ## Not run: cl <- makeSOCKcluster(rep("localhost", 4)) clusterEvalQ(cl, library(lme4)) clusterSetupSPRNG(cl) rr <- PBrefdist(beet0, beet_no.harv, nsim=20) stopCluster(cl) ## End(Not run) ## Above, 4 cpu's are used and 5 simulations are made on each cpu.