confint.merMod {lme4} | R Documentation

## 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 bootstrap function) `"theta_"` or `"beta_"` to specify variance-covariance or fixed effects parameters only: see the `which` parameter of `profile`.
`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 `bootMer` for details.
`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 `profile`).
`...` | additional parameters to be passed to `profile.merMod` or `bootMer`, respectively.

### Details

Depending on the `method` specified, `confint()` computes confidence intervals by

`"profile"`:

  computing a likelihood profile and finding the appropriate cutoffs based on the likelihood ratio test;

`"Wald"`:

  approximating the confidence intervals (of fixed-effect parameters only; all variance-covariance parameters CIs will be returned as `NA`) based on the estimated local curvature of the likelihood surface;

`"boot"`:

  performing parametric bootstrapping with confidence intervals computed from the bootstrap distribution according to `boot.type` (see `bootMer`, `boot.ci`).

### Value

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

### Note

The default method `"profile"` amounts to

        confint(profile(object, which=parm), signames=oldNames, ...),
                level, zeta)

where the `profile` method `profile.merMod` does almost all the computations. Therefore it is typically advisable to store the profile(.) result, say in `pp`, and then use `confint(pp, level=*)` e.g., for different levels.

### 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

[Package lme4 version 1.1-12 Index]