Description Usage Arguments Details Value Author(s) See Also Examples
Computes confidence intervals from the profiled likelihood for one or more parameters in a cumulative link model, or plots the profile likelihood.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## S3 method for class 'clm'
confint(object, parm, level = 0.95,
type = c("profile", "Wald"), trace = FALSE, ...)
## S3 method for class 'profile.clm'
confint(object, parm = seq_len(nprofiles),
level = 0.95, ...)
## S3 method for class 'clm'
profile(fitted, which.beta = seq_len(nbeta),
which.zeta = seq_len(nzeta), alpha = 0.001,
max.steps = 50, nsteps = 8, trace = FALSE, step.warn = 5,
control = list(), ...)
## S3 method for class 'profile.clm'
plot(x, which.par = seq_len(nprofiles),
level = c(0.95, 0.99), Log = FALSE, relative = TRUE, root =
FALSE, fig = TRUE, approx = root, n = 1e3,
ask = prod(par("mfcol")) < length(which.par) && dev.interactive(),
..., ylim = NULL)

object, fitted, x 
a fitted 
parm, which.par, which.beta, which.zeta 
a numeric or character vector indicating which regression
coefficients should be profiled. By default all coefficients are
profiled. Ignored for 
level 
the confidence level. For the 
type 
the type of confidence interval. 
trace 
if 
Log 
should the profile likelihood be plotted on the logscale? 
relative 
should the relative or the absolute likelihood be plotted? 
root 
should the (approximately linear) likelihood root statistic be plotted? 
approx 
should the Gaussian or quadratic approximation to the (log) likelihood be included? 
fig 
should the profile likelihood be plotted? 
ask 
logical; if 
n 
the no. points used in the spline interpolation of the profile likelihood. 
ylim 
overrules default ylimits on the plot of the profile likelihood. 
alpha 
the likelihood is profiled in the 100*(1alpha)% confidence region as determined by the profile likelihood. 
control 
a list of control parameters for 
max.steps 
the maximum number of profiling steps in each direction for each parameter. 
nsteps 
the (approximate) number of steps to take in each direction of the
profile for each parameter.
The step length is determined accordingly assuming a quadratic
approximation to the loglikelihood function.
The actual number of steps will often be close to 
step.warn 
a warning is issued if the number of steps in each
direction (up or down) for a parameter is less than

... 
additional arguments to be parsed on to methods. 
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.
confint
:
A matrix with columns giving lower and upper confidence
limits for each parameter. These will be labelled as (1level)/2 and
1  (1level)/2 in % (by default 2.5% and 97.5%).
plot.profile.clm
invisibly returns the profile object, i.e., a
list of data.frame
s with an lroot
component for
the likelihood root statistic and a matrix par.vals
with
values of the parameters.
Rune Haubo B Christensen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  ## Accurate profile likelihood confidence intervals compared to the
## conventional Wald intervals:
fm1 < clm(rating ~ temp * contact, data = wine)
confint(fm1) ## type = "profile"
confint(fm1, type = "Wald")
pr1 < profile(fm1)
confint(pr1)
## plotting the profiles:
par(mfrow = c(2, 2))
plot(pr1, root = TRUE) ## check for linearity
par(mfrow = c(2, 2))
plot(pr1)
par(mfrow = c(2, 2))
plot(pr1, approx = TRUE)
par(mfrow = c(2, 2))
plot(pr1, Log = TRUE)
par(mfrow = c(2, 2))
plot(pr1, Log = TRUE, relative = FALSE)
## Not likely to be useful but allowed for completeness:
par(mfrow = c(2, 2))
plot(pr1, Log = FALSE, relative = FALSE)
## Example from polr in package MASS:
## Fit model from polr example:
if(require(MASS)) {
fm1 < clm(Sat ~ Infl + Type + Cont, weights = Freq,
data = housing)
pr1 < profile(fm1)
confint(pr1)
par(mfrow=c(2,2))
plot(pr1)
}

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