rpf.grm {rpf} | R Documentation |
Create a graded response model
Description
For outcomes k in 0 to K, slope vector a, intercept vector c, and latent ability vector theta, the response probability function is
\mathrm P(\mathrm{pick}=0|a,c,\theta) = 1- \mathrm P(\mathrm{pick}=1|a,c_1,\theta)
\mathrm P(\mathrm{pick}=k|a,c,\theta) = \frac{1}{1+\exp(-(a\theta + c_k))} - \frac{1}{1+\exp(-(a\theta + c_{k+1}))}
\mathrm P(\mathrm{pick}=K|a,c,\theta) = \frac{1}{1+\exp(-(a\theta + c_K))}
Usage
rpf.grm(outcomes = 2, factors = 1, multidimensional = TRUE)
Arguments
outcomes |
The number of choices available |
factors |
the number of factors |
multidimensional |
whether to use a multidimensional model.
Defaults to |
Details
The graded response model was designed for a item with a series of
dependent parts where a higher score implies that easier parts of
the item were surmounted. If there is any chance your polytomous
item has independent parts then consider rpf.nrm
.
If your categories cannot cross then the graded response model
provides a little more information than the nominal model.
Stronger a priori assumptions offer provide more power at the cost
of flexibility.
Value
an item model
See Also
Other response model:
rpf.drm()
,
rpf.gpcmp()
,
rpf.grmp()
,
rpf.lmp()
,
rpf.mcm()
,
rpf.nrm()
Examples
spec <- rpf.grm()
rpf.prob(spec, rpf.rparam(spec), 0)