profile.clmm: Confidence intervals and profile likelihoods for the standard deviation for the random term in cumulative link mixed models

Description

Computes confidence intervals from the profiled likelihood for the standard devation for the random term in a fitted cumulative link mixed model, or plots the associated profile likelihood function.

Usage

## S3 method for class 'profile.clmm':
confint(object, parm = seq_along(Pnames), level = 0.95, ...)

## S3 method for class 'clmm':
profile(fitted, alpha = 0.01, range, nSteps = 20, trace = 1, \dots)

## S3 method for class 'profile.clmm':
plot(x, parm = seq_along(Pnames), level = c(0.95, 0.99),
        Log = FALSE, relative = TRUE, fig = TRUE, n = 1e3, ..., ylim = NULL)

Arguments

object

a fitted profile.clmm object.

fitted

a fitted clmm object.

a profile.clmm object.

parm

For confint.profile.clmm: a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. Currently only "stDev"

level

the confidence level required. Observe that the model has to be
    profiled in the appropriate region; otherwise the limits are
    NA.

trace

logical. Should profiling be traced? Defaults to TRUE due to
    the time consuming nature of the computation.

alpha

Determines the range of profiling. By default the
    likelihood is profiled approximately in the 99% confidence interval
    region as determined by the Wald approximation. This is usually
    sufficient for 95% profile likelihood confidence limits.

range

if range is specified, this overrules the range
    computation based on alpha. range should be all
    positive and stDev is profiled in range(range).

nSteps

the number of points at which to profile the likelihood
    function. This determines the resolution and accuracy of the profile
    likelihood function; higher values gives a higher resolution, but
    also longer computation times.

Log

should the profile likelihood be plotted on the log-scale?

relative

should the relative or the absolute likelihood be
    plotted?

fig

should the profile likelihood be plotted?

n

the no. points used in the spline interpolation of the
    profile likelihood for plotting.

ylim

overrules default y-limits on the plot of the profile
    likelihood.

...

additional argument(s), e.g. graphical parameters for the
    plot method.

`Value`

confint:
  A matrix with columns giving lower and upper confidence
  limits. These will be labelled as (1-level)/2 and
  1 - (1-level)/2 in % (by default 2.5% and 97.5%).

  plot.profile.clm invisibly returns the profile object.

`Details`

A confint.clmm method deliberately does not exist due to the
  time consuming nature of the computations. The user is required to
  compute the profile object first and then call confint on the
  profile object to obtain profile likelihood confidence intervals.

  In plot.profile.clm: at least one of Log and
  relative arguments have to be TRUE.

`See Also`

profile and confint

`Examples`

Run this codeoptions(contrasts = c("contr.treatment", "contr.poly"))

data(cbpp, package = "lme4")
cbpp2 <- rbind(cbpp[,-(2:3)], cbpp[,-(2:3)])
cbpp2 <- within(cbpp2, {
    incidence <- as.factor(rep(0:1, each=nrow(cbpp)))
    freq <- with(cbpp, c(incidence, size - incidence))
})

## Fit with Laplace approximation:
fm1 <- clmm(incidence ~ period, random = herd, weights = freq,
            data = cbpp2, Hess = 1)

pr.fm1 <- profile(fm1)
confint(pr.fm1)

par(mfrow = c(2,2))
plot(pr.fm1)
plot(pr.fm1, Log=TRUE, relative = TRUE)
plot(pr.fm1, Log=TRUE, relative = FALSE)
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