lme4 (version 1.1-5)

profile-methods: Profile method for merMod objects

Description

Methods for profile() of [ng]lmer fitted models.

The log() method transforms a lmer profile to the scale of the logarithm of the standard deviation of the random effects, i.e. it returns a profile with all the .sigNN parameters replaced by .lsigNN. The forward and backward splines for these parameters are recalculated.

Usage

## S3 method for class 'merMod':
profile(fitted, which = 1:nptot, alphamax = 0.01,
	maxpts = 100, delta = cutoff/8, verbose = 0, devtol = 1e-09,
        maxmult = 10, startmethod = "prev", optimizer = "bobyqa",
	signames = TRUE, ...)
## S3 method for class 'thpr':
as.data.frame(x, ...)
## S3 method for class 'thpr':
log(x, base = exp(1))

Arguments

fitted

a fitted model, e.g., the result of lmer(..).

which

integer or character vector indicating which parameters to profile: default is all parameters. For integer, i.e., indexing, the parameters are ordered as follows: [object Object],[object Object],[object Object] In addition, which

alphamax

a number in $(0,1)$, such that 1 - alphamax is the maximum alpha value for likelihood ratio confidence regions; used to establish the range of values to be profiled.

maxpts

maximum number of points (in each direction, for each parameter) to evaluate in attempting to construct the profile.

delta

stepping scale for deciding on next point to profile.

verbose

level of output from internal calculations.

devtol

tolerance for fitted deviances less than baseline (supposedly minimum) deviance.

maxmult

maximum multiplier of the original step size allowed, defaults to 10.

startmethod

method for picking starting conditions for optimization (STUB).

optimizer

(character or function) optimizer to use (see lmer for details).

signames

logical indicating if abbreviated names of the form .sigNN should be used; otherwise, names are more meaningful (but longer) of the form (sd|cor)_(effects)|(group). Note that some code for profile transformations (e.

...

potential further arguments for various methods.

x

an object of class thpr (i.e., output of profile)

base

the base of the logarithm. Defaults to natural logarithms.

Value

• profile() returns an object of S3 class "thpr", data.frame-like. Methods for such a profile object are notably confint(), the three plots methods xyplot, densityplot, and splom.

  Further, log() (see above) and as.data.frame().

Details

Methods for function profile (package stats), here for profiling (fitted) mixed effect models.

See Also

For (more expensive) alternative confidence intervals: bootMer.

Examples

fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE)
system.time(
  tpr  <- profile(fm01ML, optimizer="Nelder_Mead", which="beta_")
)## fast; as only *one* beta parameter is profiled over
## full profiling (default which means 'all) needs
## ~2.6s (on a 2010 Macbook Pro)
system.time( tpr  <- profile(fm01ML))
## ~1s, + possible warning about bobyqa convergence
(confint(tpr) -> CIpr)
stopifnot(all.equal(CIpr,
  array(c(12.1985292, 38.2299848, 1486.4515,
          84.0630513, 67.6576964, 1568.54849), dim = 3:2,
        dimnames = list(c(".sig01", ".sigma", "(Intercept)"),
                        c("2.5 %", "97.5 %"))),
                    tol= 1e-07))# 1.37e-9 {64b}
require(lattice)
xyplot(tpr)
xyplot(tpr, absVal=TRUE) # easier to see conf.int.s (and check symmetry)
densityplot(tpr, main="densityplot( profile(lmer(..)) )")
splom(tpr)
doMore <- lme4:::testLevel() > 1if(doMore) { ## not typically, for time constraint reasons
 ## Batch and residual variance only
 system.time(tpr2 <- profile(fm01ML, which=1:2, optimizer="Nelder_Mead"))
 print( xyplot(tpr2) )
 print( xyplot(log(tpr2)) )# log(sigma) is better

 ## GLMM example
 gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
	     data = cbpp, family = binomial)
 ## running time ~9 seconds on a modern machine:
 print( system.time(pr4 <- profile(gm1)) )
 print( xyplot(pr4,layout=c(5,1),as.table=TRUE) )
 print( xyplot(log(pr4), absVal=TRUE) ) # log(sigma_1)
 print( splom(pr4) )
 print( system.time( # quicker: only sig01 and one fixed effect
     pr2 <- profile(gm1, which=c("theta_", "period2"))))
 print( confint(pr2) )
}