calctheta: calculates EAP theta estimates and associated standard errors

In lordif: Logistic Ordinal Regression Differential Item Functioning using IRT

Description

Calculates Expected A Posteriori (EAP) theta estimates and associated standard error estimates (posterior standard deviations).

Usage

  calctheta(ipar, resp.data, theta, prior.mean = 0, prior.sd = 1, model = "GRM")

Arguments

ipar	a data frame containing the following columns: a, cb1, cb2,..., cb(maxCat)
resp.data	a data frame containing item responses
theta	a theta grid (quadrature points)
prior.mean	prior mean
prior.sd	prior standard deviation
model	IRT model, either "GRM" or "GPCM")

Details

Calculates EAP theta estimates and standard error estimates based on the input item parameters (ipar), the item response data (resp.data), and the IRT model specified ("GRM" or "GPCM").

Value

A list object with the following components

EAP	Expected A Posteriori estimates of theta
SE	Standard Error estimates

Note

Some missing item responses (NA) are allowed.

Author(s)

Seung W. Choi <[e-mail]>

References

Bock, R. D. & Mislevy, R. J. (1982). Adaptive EAP Estimation of Ability in a Microcomputer Environment. Applied Psychological Measurement, 6(4), 431-444.

See Also

calcprob, probgrm, probgpcm

Examples

  ## Not run: calctheta(ipar,resp.data,model="GPCM")

Example output

Loading required package: mirt
Loading required package: stats4
Loading required package: lattice
Loading required package: rms
Loading required package: Hmisc
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:base’:

    format.pval, units

Loading required package: SparseM

Attaching package: ‘SparseM’

The following object is masked from ‘package:base’:

    backsolve

lordif documentation built on May 2, 2019, 2:13 p.m.