confint.clm2 | R Documentation |
Computes confidence intervals from the profiled likelihood for one or more parameters in a fitted cumulative link model, or plots the profile likelihood function.
## S3 method for class 'clm2'
confint(object, parm, level = 0.95, whichL = seq_len(p),
whichS = seq_len(k), lambda = TRUE, trace = 0, ...)
## S3 method for class 'profile.clm2'
confint(object, parm = seq_along(Pnames), level = 0.95, ...)
## S3 method for class 'clm2'
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.clm2'
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 |
fitted |
a fitted |
x |
a |
parm |
not used in For For |
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 |
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 when constructing confidence intervals so fairly long steps can provide high accuracy. |
stepWarn |
a warning is issued if the no. steps in each direction
(up or down) for a parameter is less than |
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 |
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.clm2
: at least one of Log
and
relative
arguments have to be TRUE
.
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.clm2
invisibly returns the profile object.
Rune Haubo B Christensen
profile
and confint
options(contrasts = c("contr.treatment", "contr.poly"))
## 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 <- clm2(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:
if(require(MASS)) {
fm1 <- clm2(Sat ~ Infl + Type + Cont, scale = ~ Cont, weights = Freq,
data = housing)
pr1 <- profile(fm1)
confint(pr1)
par(mfrow=c(2,2))
plot(pr1)
}
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