diffIRT (version 1.5)

factest: Estimating factor scores for diffIRT models

Description

This function estimates the person drift rate and person boundary separation for diffIRT objects.

Usage

factest(object,start=NULL,se=F, control=list())

Arguments

object

A diffIRT object for which factor scores need to be estimated.

start

If NULL starting values are automatically chosen. Otherwise, start should be a vector of size 2 x N, where N denotes the number of subjects. The first N elements correspond to the starting values for person boundary separation (ap), the next N elements correspond to the starting values for person drift rate (vp. NA are allowed.

se

Logical; Denoting whether standard errors of the parameters should be estimated (can be time consuming, therefore, default is F).

control

a list of control values for the optimisation

Value

Function factest returns a matrix of parameter estimates and - if se=T - standard errors.

Details

factest returns empirical Bayes estimates of the person drift rate and the person boundary separation. See diffIRT for more explanation concerning the parameters in the D-diffusion and Q-diffusion IRT model.

References

Navarro, D.J. & Fuss, I.G. (2009). Fast and accurate calculations for first-passagetimes in Wiener diffusion models. Journal of mathematical psychology, 53, 222-230.

Tuerlinckx, F., & De Boeck, P. (2005). Two interpretations of the discrimination parameter. Psychometrika, 70, 629-650.

van der Maas, H.L.J., Molenaar, D., Maris, G., Kievit, R.A., & Borsboom, D. (2011). Cognitive Psychology Meets Psychometric Theory: On the Relation Between Process Models for Decision Making and Latent Variable Models for Individual Differences. Psychological Review, 118, 339-356.

See Also

diffIRT for fitting diffusion IRT models. simdiff for simulating data according to the D-diffusion or Q-diffusion IRT model. QQdiff and RespFit for model fit assesment.

Examples

## Not run: 
#  # simulate data accroding to D-diffusion model
# data=simdiff(N=100,nit=10,model="D")                   
# 
# # fit an unconstrained model
# res1=diffIRT(data$rt,data$x,model="D")          
# 
# # estimate factor scores
# fs=factest(res1) 
# ## End(Not run)