rpf (version 0.5)

rpf.1dim.fit: Calculate item and person Rasch fit statistics

Description

Note: These statistics are only appropriate if all discrimination parameters are fixed equal and items are conditionally independent (see ChenThissen1997). A best effort is made to cope with missing data.

Usage

rpf.1dim.fit(spec, params, responses, scores, margin, group = NULL,
  wh.exact = TRUE)

Arguments

spec

list of item models

params

matrix of item parameters, 1 per column

responses

persons in rows and items in columns

scores

model derived person scores

margin

for people 1, for items 2

group

spec, params, data, and scores can be provided in a list instead of as arguments

wh.exact

whether to use the exact Wilson-Hilferty transformation

Details

Exact distributional properties of these statistics are unknown (Masters & Wright, 1997, p. 112). For details on the calculation, refer to Wright & Masters (1982, p. 100).

The Wilson-Hilferty transformation is biased for less than 25 items. Consider wh.exact=FALSE for less than 25 items.

References

Masters, G. N. & Wright, B. D. (1997). The Partial Credit Model. In W. van der Linden & R. K. Kambleton (Eds.), Handbook of modern item response theory (pp. 101-121). Springer.

Wilson, E. B., & Hilferty, M. M. (1931). The distribution of chi-square. Proceedings of the National Academy of Sciences of the United States of America, 17, 684-688.

Wright, B. D. & Masters, G. N. (1982). Rating Scale Analysis. Chicago: Mesa Press.

Examples

data(kct)
responses <- kct.people[,paste("V",2:19, sep="")]
rownames(responses) <- kct.people$NAME
colnames(responses) <- kct.items$NAME
scores <- kct.people$MEASURE
params <- cbind(1, kct.items$MEASURE, logit(0), logit(1))
rownames(params) <- kct.items$NAME
items<-list()
items[1:18] <- rpf.drm()
params[,2] <- -params[,2]
rpf.1dim.fit(items, t(params), responses, scores, 2, wh.exact=TRUE)