twinstim
objectstwinstim_profile.Rd
Function to compute estimated and profile likelihood based confidence
intervals for twinstim
objects. Computations might be cumbersome!
WARNING: the implementation is not well tested, simply uses
optim
(ignoring optimizer settings from the original fit),
and does not return the complete set of coefficients at each grid point.
# S3 method for twinstim
profile(fitted, profile, alpha = 0.05,
control = list(fnscale = -1, maxit = 100, trace = 1),
do.ltildeprofile=FALSE, ...)
an object of class "twinstim"
.
a list with elements being numeric vectors of length 4. These vectors must
have the form c(index, lower, upper, gridsize)
.
index
:index of the parameter to be profiled in the vector coef(fitted)
.
lower, upper
:lower/upper limit of the grid on which the profile log-likelihood is
evaluated. Can also be NA
in which case lower/upper
equals
the lower/upper bound of the respective 0.3 % Wald confidence interval
(+-3*se).
gridsize
:grid size of the equally spaced grid between lower and upper. Can also be 0 in which case the profile log-likelihood for this parameter is not evaluated on a grid.
\((1-\alpha)\%\) profile likelihood based confidence intervals are computed. If alpha <= 0, then no confidence intervals are computed. This is currently not implemented.
control object to use in optim
for the profile log-likelihood
computations. It might be necessary to control maxit
or
reltol
in order to obtain results in finite time.
If TRUE
calculate profile likelihood as
well. This might take a while, since an optimisation for all other
parameters has to be performed. Useful for likelihood based
confidence intervals. Default: FALSE
.
unused (argument of the generic).
list with profile log-likelihood evaluations on the grid, and
-- not implemented yet --
highest likelihood and Wald confidence intervals.
The argument profile
is also returned.
Michael Höhle
# profiling takes a while
if (FALSE) {
#Load the twinstim model fitted to the IMD data
data("imdepi", "imdepifit")
# for profiling we need the model environment
imdepifit <- update(imdepifit, model=TRUE)
#Generate profiling object for a list of parameters for the new model
names <- c("h.(Intercept)","e.typeC")
coefList <- lapply(names, function(name) {
c(pmatch(name,names(coef(imdepifit))),NA,NA,11)
})
#Profile object (necessary to specify a more loose convergence
#criterion). Speed things up by using do.ltildeprofile=FALSE (the default)
prof <- profile(imdepifit, coefList,
control=list(reltol=0.1, REPORT=1), do.ltildeprofile=TRUE)
#Plot result for one variable
par(mfrow=c(1,2))
for (name in names) {
with(as.data.frame(prof$lp[[name]]),
matplot(grid,cbind(profile,estimated,wald),
type="l",xlab=name,ylab="loglik"))
legend(x="bottomleft",c("profile","estimated","wald"),lty=1:3,col=1:3)
}
}