R: Compute likelihood profiles for a fitted model

profile.glmmTMB {glmmTMB} | R Documentation

## 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)

[Package glmmTMB version 0.2.2.0 Index]