# profile.clmm2

0th

Percentile

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

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.

Keywords
models
##### Usage
# S3 method for profile.clmm2
confint(object, parm = seq_along(Pnames), level = 0.95, …)# S3 method for clmm2
profile(fitted, alpha = 0.01, range, nSteps = 20, trace = 1, …)# S3 method for profile.clmm2
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.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.

##### Details

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.

##### 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.clm2 invisibly returns the profile object.

##### See Also

profile and confint

##### Aliases
• profile.clmm2
• confint.clmm2
• confint.profile.clmm2
• profile.clmm2
• plot.profile.clmm2
##### Examples
# NOT RUN {
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)
}

# }

Documentation reproduced from package ordinal, version 2019.12-10, License: GPL (>= 2)

### Community examples

Looks like there are no examples yet.