mirt (version 1.8)

PLCI.mirt: Compute profiled-likelihood confidence intervals

Description

Computes profiled-likelihood based confidence intervals. Supports the inclusion of prior parameter distributions as well as equality constraints.

Usage

PLCI.mirt(mod, alpha = 0.05, parnum = NULL, plot = FALSE, npts = 24,
  ...)

Arguments

mod

a converged mirt model

alpha

two-tailed alpha critical level

parnum

a numeric vector indicating which parameters to estimate. Use mod2values to determine parameter numbers. If NULL, all possible parameters are used

plot

logical; plot the parameter relationship in the likelihood space for two parameters?

npts

number of points to evaluate and plot if plot = TRUE

...

additional arguments to pass to the estimation functions

See Also

boot.mirt

Examples

mirtCluster() #use all available cores to estimate CI's in parallel
dat <- expand.table(LSAT7)
mod <- mirt(dat, 1)

result <- PLCI.mirt(mod)
result

mod2 <- mirt(Science, 1)
result2 <- PLCI.mirt(mod2)
result2

#only estimate CI's slopes
sv <- mod2values(mod2)
parnum <- sv$parnum[sv$name == 'a1']
result3 <- PLCI.mirt(mod2, parnum=parnum)
result3