confint.clmmOld: Confidence intervals and profile likelihoods for the standard...
In ordinal: Regression Models for Ordinal Data

Description Usage Arguments Details Value Author(s) See Also Examples

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.

## S3 method for class 'profile.clmm2'
confint(object, parm = seq_along(Pnames), level = 0.95, ...)

## S3 method for class 'clmm2'
profile(fitted, alpha = 0.01, range, nSteps = 20, trace = 1, ...)

## S3 method for class 'profile.clmm2'
plot(x, parm = seq_along(Pnames), level = c(0.95, 0.99),
        Log = FALSE, relative = TRUE, fig = TRUE, n = 1e3, ..., ylim = NULL)

`object`	a fitted `profile.clmm2` object.
`fitted`	a fitted `clmm2` object.
`x`	a `profile.clmm2` object.
`parm`	For `confint.profile.clmm2`: 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"` or `1` are supported. For `plot.profile.clmm2`: a specification of which parameters the profile likelihood are to be plotted for, either a vector of numbers or a vector of names. If missing, all parameters are considered. Currently only `"stDev"` or `1` are supported.
`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.

A confint.clmm2 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.clm2: at least one of Log and relative arguments have to be TRUE.

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.clm2 invisibly returns the profile object.

Rune Haubo B Christensen

profile and confint

options(contrasts = c("contr.treatment", "contr.poly"))

if(require(lme4)) { ## access cbpp data
    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 <- clmm2(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)
}