spaMM (version 3.5.0)

get_RLRsim_args: Extractors of arguments for functions from package RLRsim

Description

get_RLRsim_args extracts a list of arguments suitable for a call to RLRsim::RLRTSim() or RLRsim::LRTSim(). These functions use an efficient simulation procedure to compute restricted or marginal likelihood ratio tests, respectively, comparing a fixed-effect model and a mixed-effect model with one random effect. They are notably used to test for the presence of one random effect, although the models compared by marginal likelihood (LRTSim()) may differ both in their random and in their fixed effects (as shown in the Example). The tests are exact for small samples (up to simulation error) for LMMs with no free parameters in the random effect (beyond the variance being tested), so not for time-series or spatial models with estimated correlation parameters. Heteroscedasticity of the residuals or of the random effect variance are also not taken into account by the simulation procedure (see Value field below for an hint why this is so).

get_RLRTSim_args is the older extractor, originally for RLRsim::RLRTSim() only, now handling also ML fits with a warning.

Usage

get_RLRsim_args(object, verbose=TRUE, REML=NA, ...)
get_RLRTSim_args(object, verbose=TRUE, ...)

Arguments

object

An object of class HLfit, as returned by the fitting functions in spaMM, for the more complete model to be compared.

verbose

NA or boolean; Whether to display some message or not.

REML

For programming purposes, not documented.

…

Additional arguments (currently not used).

Value

A list of arguments for a call to RLRsim::RLRTSim() or RLRsim::LRTSim(). The main arguments are the design matrices of the more complete model (the one for the fixed effect, and the ZAL matrix detailed in random-effects). The models handled by the testing procedure are the ones that are sufficiently characterized by these two matrices.

References

Crainiceanu, C. and Ruppert, D. (2004) Likelihood ratio tests in linear mixed models with one variance component, Journal of the Royal Statistical Society: Series B,66,165--185.

Examples

# NOT RUN {
## Derived from example in RLRsim::LRTSim
 set.seed(123)
 dat <- data.frame(g = rep(1:10, e = 10), x = (x<-rnorm(100)), 
                   y = 0.1 * x + rnorm(100))
 m <- fitme(y ~ x + (1|g), data=dat)
 m0 <- fitme(y ~ 1, data=dat) 
 (obs.LRT <- 2*(logLik(m)-logLik(m0)))
 args <- get_RLRsim_args(m)
 sim.LRT <- do.call(RLRSim::RLRTsim, args )
 (pval <- (sum(sim.LRT >= obs.LRT) + 1) / (length(sim.LRT) + 1))
# }