profile.glmmTMB: Compute likelihood profiles for a fitted model

In glmmTMB: Generalized Linear Mixed Models using Template Model Builder

Compute likelihood profiles for a fitted model

Description

Compute likelihood profiles for a fitted model

Usage

## S3 method for class 'glmmTMB'
profile(
  fitted,
  parm = NULL,
  level_max = 0.99,
  npts = 8,
  stepfac = 1/4,
  stderr = NULL,
  trace = FALSE,
  parallel = c("no", "multicore", "snow"),
  ncpus = getOption("profile.ncpus", 1L),
  cl = NULL,
  ...
)

## S3 method for class 'profile.glmmTMB'
confint(object, parm = NULL, level = 0.95, ...)

Arguments

fitted	a fitted glmmTMB object
parm	which parameters to profile, specified by index (position) by name (matching the row/column names of vcov(object,full=TRUE)) as "theta_" (random-effects variance-covariance parameters) or "beta_" (conditional and zero-inflation parameters)
level_max	maximum confidence interval target for profile
npts	target number of points in (each half of) the profile (approximate)
stepfac	initial step factor (fraction of estimated standard deviation)
stderr	standard errors to use as a scaling factor when picking step sizes to compute the profile; by default (if stderr is NULL, or NA for a particular element), uses the estimated (Wald) standard errors of the parameters
trace	print tracing information? If trace=FALSE or 0, no tracing; if trace=1, print names of parameters currently being profiled; if trace>1, turn on tracing for the underlying tmbprofile function
parallel	method (if any) for parallel computation
ncpus	number of CPUs/cores to use for parallel computation
cl	cluster to use for parallel computation
...	additional arguments passed to tmbprofile
object	a fitted profile (profile.glmmTMB) object
level	confidence level

Details

Fits natural splines separately to the points from each half of the profile for each specified parameter (i.e., values above and below the MLE), then finds the inverse functions to estimate the endpoints of the confidence interval

Value

An object of class profile.glmmTMB, which is also a data frame, with columns .par (parameter being profiled), .focal (value of focal parameter), value (negative log-likelihood).

Examples

## Not run: 
m1 <- glmmTMB(count~ mined + (1|site),
       zi=~mined, family=poisson, data=Salamanders)
salamander_prof1 <- profile(m1, parallel="multicore",
                            ncpus=2, trace=1)
## testing
salamander_prof1 <- profile(m1, trace=1,parm=1)
salamander_prof1M <- profile(m1, trace=1,parm=1, npts = 4)
salamander_prof2 <- profile(m1, parm="theta_")


## End(Not run)
salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB"))
if (require("ggplot2")) {
    ggplot(salamander_prof1,aes(.focal,sqrt(value))) +
        geom_point() + geom_line()+
        facet_wrap(~.par,scale="free_x")+
    geom_hline(yintercept=1.96,linetype=2)
}
salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB"))
confint(salamander_prof1)
confint(salamander_prof1,level=0.99)

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