R: Calculate reference distribution using parametric bootstrap

pb-refdist {pbkrtest} | R Documentation

## Calculate reference distribution using parametric bootstrap

### Description

Calculate reference distribution of likelihood ratio statistic in mixed effects models using parametric bootstrap

### Usage

    PBrefdist(largeModel, smallModel, nsim = 1000, seed = NULL, cl = NULL,
      details = 0)

    ## S3 method for class 'lm'
    PBrefdist(largeModel, smallModel, nsim = 1000, seed = NULL,
      cl = NULL, details = 0)

    ## S3 method for class 'merMod'
    PBrefdist(largeModel, smallModel, nsim = 1000, seed = NULL,
      cl = NULL, details = 0)

    ## S3 method for class 'mer'
    PBrefdist(largeModel, smallModel, nsim = 1000, seed = NULL,
      cl = NULL, details = 0)

### Arguments

`largeModel` | A linear mixed effects model as fitted with the `lmer()` function in the lme4 package. This model muse be larger than `smallModel` (see below).
`smallModel` | A linear mixed effects model as fitted with the `lmer()` function in the lme4 package. This model muse be smaller than `largeModel` (see above).
`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.

### Details

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.

### Value

A numeric vector

### Author(s)

Søren Højsgaard sorenh@math.aau.dk

### References

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/

### See Also

`PBmodcomp`, `KRmodcomp`

### Examples

    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: Many more 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.

[Package pbkrtest version 0.4-7 Index]