lme4 (version 1.1-10)

confint.merMod: Compute Confidence Intervals for Parameters of a [ng]lmer Fit

Description

Compute confidence intervals on the parameters of a *lmer() model fit (of class"merMod").

Usage

## S3 method for class 'merMod':
confint(object, parm, level = 0.95,
	method = c("profile", "Wald", "boot"), zeta,
	nsim = 500,
        boot.type = c("perc","basic","norm"),
        FUN = NULL, quiet = FALSE,
	oldNames = TRUE, ...)

Arguments

object

a fitted [ng]lmer model

parm

parameters for which intervals are sought. Specified by an integer vector of positions, character vector of parameter names, or (unless doing parametric bootstrapping with a user-specified boo

level

confidence level $< 1$, typically above 0.90.

method

a character string determining the method for computing the confidence intervals.

zeta

(for method = "profile" only:) likelihood cutoff (if not specified, as by default, computed from level).

nsim

number of simulations for parametric bootstrap intervals.

FUN

bootstrap function; if NULL, an internal function that returns the fixed-effect parameters as well as the random-effect parameters on the standard deviation/correlation scale will be used. See

boot.type

bootstrap confidence interval type, as described in boot.ci. (Methods stud and bca are unavailable because they require additional components to be calculated.)

quiet

(logical) suppress messages about computationally intensive profiling?

oldNames

(logical) use old-style names for variance-covariance parameters, e.g. ".sig01", rather than newer (more informative) names such as "sd_(Intercept)|Subject"? (See signames argument to

...

additional parameters to be passed to profile.merMod or bootMer, respectively.

Value

• a numeric table (matrix with column and row names) of confidence intervals; the confidence intervals are computed on the standard deviation scale.

Details

Depending on the method specified, confint() computes confidence intervals by [object Object],[object Object],[object Object]

Examples

fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
fm1W <- confint(fm1, method="Wald")# very fast, but ....
fm1W
testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1
if(interactive() || testLevel >= 3) {
 ## ~20 seconds, MacBook Pro laptop
 system.time(fm1P <- confint(fm1, method="profile", ## default
                             oldNames = FALSE))
 ## ~ 40 seconds
 system.time(fm1B <- confint(fm1,method="boot",
                             .progress="txt", PBargs=list(style=3)))
} else
  load(system.file("testdata","confint_ex.rda",package="lme4"))
fm1P
fm1B

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