Calculate confidence intervals
Calculate confidence intervals
## 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,
parallel = c("no", "multicore", "snow"),
ncpus = getOption("profile.ncpus", 1L),
cl = NULL,
full = FALSE,
...
)object |
|
parm |
which parameters to profile, specified
Parameter indexing by number may give unusual results when
some parameters have been fixed using the |
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 ? |
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 |
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 (see the
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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.