R: Create a graded response model

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

P(pick=0|a,c,th) = 1-P(pick=1|a,c_1,th)

P(pick=k|a,c,th) = 1/(1+exp(-(a th + c_k))) - 1/(1+exp(-(a th + c_(k+1))))

P(pick=K|a,c,th) = 1/(1+exp(-(a th + 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 `TRUE`.

### 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

### Examples

    spec <- rpf.grm()
    rpf.prob(spec, rpf.rparam(spec), 0)

[Package rpf version 0.59 Index]