# profile.clmm2

##### 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

##### 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)*