rpf (version 0.3)

multinomialFit: Multinomial fit test

Description

The p-value is known to become poorly calibrated as the number of cells becomes large (e.g. more than 1000). For accurate p-values, you can conduct a Monte-Carlo simulation study (see examples).

Usage

multinomialFit(grp, ..., method = "lr", log = TRUE, .twotier = TRUE)

Arguments

grp

a list with the spec, param, mean, and cov describing the group

...

Not used. Forces remaining arguments to be specified by name.

method

lr (default) or pearson

log

whether to report p-value in log units

.twotier

whether to use the two-tier optimization (default TRUE)

Details

Rows with missing data are ignored.

The full information test is described in Bartholomew & Tzamourani (1999, Section 3).

References

Bartholomew, D. J., & Tzamourani, P. (1999). The goodness-of-fit of latent trait models in attitude measurement. Sociological Methods and Research, 27, 525-546.

Examples

# Create an example IFA group
grp <- list(spec=list())
grp$spec[1:10] <- rpf.grm()
grp$param <- sapply(grp$spec, rpf.rparam)
colnames(grp$param) <- paste("i", 1:10, sep="")
grp$mean <- 0
grp$cov <- diag(1)
grp$free <- grp$param != 0
grp$data <- rpf.sample(1000, grp=grp)

# Monte-Carlo simulation study
mcReps <- 3    # increase this to 10,000 or so
stat <- rep(NA, mcReps)
for (rx in 1:mcReps) {
   t1 <- grp
   t1$data <- rpf.sample(grp=grp)
   stat[rx] <- multinomialFit(t1)$statistic
}
sum(multinomialFit(grp)$statistic > stat)/mcReps   # better p-value