GoF.gpcm(object, simulate.p.value = TRUE, B = 99, seed = NULL, ...)
GoF.rasch(object, B = 49, ...)gpcm or class rasch.TRUE, the reported $p$-value is based on a parametric Bootstrap approach.
Otherwise the $p$-value is based on the asymptotic chi-squared distribution.NULL, a random seed is used.GoF.gpcm or GoF.rasch with components,B argument specifying the number of Bootstrap samples used.object.simulate.p.value argument (returned on for class GoF.gpcm).GoF.gpcm).GoF.gpcm and GoF.rasch perform a parametric Bootstrap test based on Pearson's chi-squared statistic defined as
$$\sum\limits_{r = 1}^{2^p} \frac{{O(r) - E(r)}^2}{E(r)},$$ where $r$
represents a response pattern, $O(r)$ and $E(r)$ represent the observed and expected frequencies,
respectively and $p$ denotes the number of items. The Bootstrap approximation to the reference distribution is preferable compared with
the ordinary Chi-squared approximation since the latter is not valid especially for large number of items
(=> many response patterns with expected frequencies smaller than 1).
In particular, the Bootstrap test is implemented as follows:
[object Object],[object Object],[object Object],[object Object]
Furthermore, in GoF.gpcm when simulate.p.value = FALSE, then the $p$-value is based on the asymptotic
chi-squared distribution.person.fit,
item.fit,
margins,
gpcm,
rasch## GoF for the Rasch model for the LSAT data:
fit <- rasch(LSAT)
GoF.rasch(fit)Run the code above in your browser using DataLab