rpf.nrm: Create a nominal response model In rpf: Response Probability Functions

Description

This function instantiates a nominal response model.

Usage

 1 rpf.nrm(outcomes = 3, factors = 1, T.a = "trend", T.c = "trend") 

Arguments

 outcomes The number of choices available factors the number of factors T.a the T matrix for slope parameters T.c the T matrix for intercept parameters

Details

The transformation matrices T.a and T.c are chosen by the analyst and not estimated. The T matrices must be invertible square matrices of size outcomes-1. As a shortcut, either T matrix can be specified as "trend" for a Fourier basis or as "id" for an identity basis. The response probability function is

a = T_a α

c = T_c γ

\mathrm P(\mathrm{pick}=k|s,a_k,c_k,θ) = C\ \frac{1}{1+\exp(-(s θ a_k + c_k))}

where a_k and c_k are the result of multiplying two vectors of free parameters α and γ by fixed matrices T_a and T_c, respectively; a_0 and c_0 are fixed to 0 for identification; and C is a normalizing factor to ensure that ∑_k \mathrm P(\mathrm{pick}=k) = 1.

an item model

References

Thissen, D., Cai, L., & Bock, R. D. (2010). The Nominal Categories Item Response Model. In M. L. Nering & R. Ostini (Eds.), Handbook of Polytomous Item Response Theory Models (pp. 43–75). Routledge.

Examples

 1 2 3 4 5 6 spec <- rpf.nrm() rpf.prob(spec, rpf.rparam(spec), 0) # typical parameterization for the Generalized Partial Credit Model gpcm <- function(outcomes) rpf.nrm(outcomes, T.c=lower.tri(diag(outcomes-1),TRUE) * -1) spec <- gpcm(4) rpf.prob(spec, rpf.rparam(spec), 0) 

rpf documentation built on Nov. 17, 2017, 4:27 a.m.