Bootstrap confidence intervals for Two-Level Mixed Models

This is a wrapper for getting CIs for multiple parameters after running bootstrap_mer, to be consistent with a similar method for the bootMer class.

Usage

# S3 method for class 'boot'
confint(
  object,
  parm,
  level = 0.95,
  type = c("norm", "basic", "perc", "bca"),
  L = NULL,
  ...
)

Arguments

object: an object returned by bootstrap_mer.
parm: a specification of which parameters are to be given confidence intervals as a vector of numbers. If missing, all parameters are considered.
level: the confidence level required.
type: character indicating the type of intervals required, as described in boot.ci. Currently "stud" is not supported.
L: empirical influence values required for type = "bca" as described in boot.ci.
...: additional argument(s) passed to boot.ci.

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter.

Examples

library(lme4)
fm01ML <- lmer(Yield ~ (1 | Batch), Dyestuff, REML = FALSE)
mySumm <- function(x) {
  c(getME(x, "beta"), sigma(x))
}

# residual bootstrap
boo_resid <- bootstrap_mer(fm01ML, mySumm, type = "residual", nsim = 100)
#> 5 occurrence(s) of: message(s) : boundary (singular) fit: see help('isSingular')
confint(boo_resid, type = "bca", L = empinf_merm(fm01ML, mySumm))
#>           2.5 %     97.5 %
#> [1,] 1497.77784 1576.23319
#> [2,]   35.37269   59.27245