# confint.clm

##### Confidence intervals and profile likelihoods for parameters in cumulative link models

Computes confidence intervals from the profiled likelihood for one or more parameters in a fitted cumulative link model, or plots the profile likelihood function.

- Keywords
- models

##### Usage

```
## S3 method for class 'clm':
confint(object, parm, level = 0.95, whichL = seq_len(p),
whichS = seq_len(k), lambda = TRUE, trace = 0, ...)
## S3 method for class 'profile.clm':
confint(object, parm = seq_along(Pnames), level = 0.95, ...)
## S3 method for class 'clm':
profile(fitted, whichL = seq_len(p), whichS = seq_len(k),
lambda = TRUE, alpha = 0.01, maxSteps = 50, delta = LrootMax/10,
trace = 0, stepWarn = 8, ...)
## S3 method for class 'profile.clm':
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
`clm`

object or a`profile.clm`

object. - fitted
- a fitted
`clm`

object. - x
- a
`profile.clm`

object. - parm
- not used in
`confint.clm`

. For`confint.profile.clm`

: 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 con - level
- the confidence level required.
- whichL
- a specification of which
*location*parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all location parameters are considered. - whichS
- a specification of which
*scale*parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all scale parameters are considered. - lambda
- logical. Should profile or confidence intervals be computed for the
link function parameter? Only used when one of the flexible link
functions are used; see the
`link`

-argument in`clm`

. - trace
- logical. Should profiling be traced?
- alpha
- Determines the range of profiling. By default the likelihood is profiled in the 99% confidence interval region as determined by the profile likelihood.
- maxSteps
- the maximum number of profiling steps in each direction (up and down) for each parameter.
- delta
- the length of profiling steps. To some extent this parameter determines the degree of accuracy of the profile likelihood in that smaller values, i.e. smaller steps gives a higher accuracy. Note however that a spline interpolation is used wh
- stepWarn
- a warning is issued if the no. steps in each direction
(up or down) for a parameter is less than
`stepWarn`

(defaults to 8 steps) because this indicates an unreliable profile. - 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.
- ylim
- overrules default y-limits on the plot of the profile likelihood.
- ...
- additional argument(s) for methods including
`range`

(for the hidden function`profileLambda`

) that sets the range of values of`lambda`

at which the likelihood should be profiled for this parameter.

##### Details

These `confint`

methods call
the appropriate profile method, then finds the
confidence intervals by interpolation of the profile traces.
If the profile object is already available, this should be used as the
main argument rather than the fitted model object itself.
In `plot.profile.clm`

: at least one of `Log`

and
`relative`

arguments have to be `TRUE`

.

##### Value

`confint`

: A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%). The parameter names are preceded with`"loc."`

or`"sca."`

to indicate whether the confidence interval applies to a location or a scale parameter.`plot.profile.clm`

invisibly returns the profile object.

##### See Also

##### Examples

```
options(contrasts = c("contr.treatment", "contr.poly"))
data(soup)
## More manageable data set:
(tab26 <- with(soup, table("Product" = PROD, "Response" = SURENESS)))
dimnames(tab26)[[2]] <- c("Sure", "Not Sure", "Guess", "Guess", "Not Sure", "Sure")
dat26 <- expand.grid(sureness = as.factor(1:6), prod = c("Ref", "Test"))
dat26$wghts <- c(t(tab26))
m1 <- clm(sureness ~ prod, scale = ~prod, data = dat26,
weights = wghts, link = "logistic")
## profile
pr1 <- profile(m1)
par(mfrow = c(2, 2))
plot(pr1)
m9 <- update(m1, link = "log-gamma")
pr9 <- profile(m9, whichL = numeric(0), whichS = numeric(0))
par(mfrow = c(1, 1))
plot(pr9)
plot(pr9, Log=TRUE, relative = TRUE)
plot(pr9, Log=TRUE, relative = TRUE, ylim = c(-4, 0))
plot(pr9, Log=TRUE, relative = FALSE)
## confint
confint(pr9)
confint(pr1)
## Extend example from polr in package MASS:
## Fit model from polr example:
data(housing, package = "MASS")
fm1 <- clm(Sat ~ Infl + Type + Cont, scale = ~ Cont, weights = Freq,
data = housing)
pr1 <- profile(fm1)
confint(pr1)
par(mfrow=c(2,2))
plot(pr1)
```

*Documentation reproduced from package ordinal, version 2011.05-10, License: GPL (>= 2)*

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