confint.glmmTMB: Calculate confidence intervals

confint.glmmTMBR Documentation

Calculate confidence intervals


Calculate confidence intervals


## S3 method for class 'glmmTMB'
  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,



glmmTMB fitted object.


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.


Confidence level.


'wald', 'profile', or 'uniroot': see Details function)


Which of the three components 'cond', 'zi' or 'other' to select. Default is to select 'all'.


(logical) add a third column with estimate ?


include dummy rows for non-estimated (mapped, rank-deficient) parameters?


method (if any) for parallel computation


number of CPUs/cores to use for parallel computation


cluster to use for parallel computation


CIs for all parameters (including dispersion) ?


arguments may be passed to profile.glmmTMB (and possibly from there to tmbprofile) or tmbroot


Available methods are


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.


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.


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


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

glmmTMB documentation built on Oct. 7, 2023, 5:07 p.m.