confint.glmmTMB: Calculate confidence intervals

In glmmTMB: Generalized Linear Mixed Models using Template Model Builder

Calculate confidence intervals

Description

Calculate confidence intervals

Usage

## S3 method for class 'glmmTMB'
confint(
  object,
  parm = NULL,
  level = 0.95,
  method = c("wald", "Wald", "profile", "uniroot"),
  component = c("all", "cond", "zi", "other"),
  estimate = TRUE,
  include_nonest = FALSE,
  parallel = c("no", "multicore", "snow"),
  ncpus = getOption("profile.ncpus", 1L),
  cl = NULL,
  full = FALSE,
  ...
)

Arguments

object	glmmTMB fitted object.
parm	which parameters to profile, specified by index (position) [after component selection for confint, if any] by name (matching the row/column names of vcov(object,full=TRUE)) as "theta_" (random-effects variance-covariance parameters), "beta_" (conditional and zero-inflation parameters), or "disp_" or "sigma" (dispersion parameters) Parameter indexing by number may give unusual results when some parameters have been fixed using the map argument: please report surprises to the package maintainers.
level	Confidence level.
method	'wald', 'profile', or 'uniroot': see Details function)
component	Which of the three components 'cond', 'zi' or 'other' to select. Default is to select 'all'.
estimate	(logical) add a third column with estimate ?
include_nonest	include dummy rows for non-estimated (mapped, rank-deficient) parameters?
parallel	method (if any) for parallel computation
ncpus	number of CPUs/cores to use for parallel computation
cl	cluster to use for parallel computation
full	CIs for all parameters (including dispersion) ?
...	arguments may be passed to profile.glmmTMB (and possibly from there to tmbprofile) or tmbroot

Details

Available methods are

"wald"

These intervals are based on the standard errors calculated for parameters on the scale of their internal parameterization depending on the family. Derived quantities such as standard deviation parameters and dispersion parameters are back-transformed. It follows that confidence intervals for these derived quantities are typically asymmetric.

"profile"

This method computes a likelihood profile for the specified parameter(s) using profile.glmmTMB; fits a spline function to each half of the profile; and inverts the function to find the specified confidence interval.

"uniroot"

This method uses the uniroot function to find critical values of one-dimensional profile functions for each specified parameter.

At present, "wald" returns confidence intervals for variance parameters on the standard deviation/correlation scale, while "profile" and "uniroot" report them on the underlying ("theta") scale: for each random effect, the first set of parameter values are standard deviations on the log scale, while remaining parameters represent correlations on the scaled Cholesky scale. For a random effects model with two elements (such as a random-slopes model, or a random effect of factor with two levels), there is a single correlation parameter \theta; the correlation is equal to \rho = \theta/\sqrt{1+\theta^2}. For random-effects terms with more than two elements, the mapping is more complicated: see https://github.com/glmmTMB/glmmTMB/blob/master/misc/glmmTMB_corcalcs.ipynb

Examples

data(sleepstudy, package="lme4")
model <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy)
model2 <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy,
    dispformula= ~I(Days>8))
confint(model)  ## Wald/delta-method CIs
confint(model,parm="theta_")  ## Wald/delta-method CIs
confint(model,parm=1,method="profile")

glmmTMB documentation built on Sept. 30, 2024, 9:34 a.m.