rpf.1dim.fit {rpf} | R Documentation |
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.
rpf.1dim.fit(spec, params, responses, scores, margin, group = NULL, wh.exact = TRUE)
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 |
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.
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.
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)