factest {diffIRT}

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

method

The optimisation method used by optim. Default "BFGS".

eps

See optim for details and default.

delta

See optim for details and default.

trace

See optim for details and default.

fnscale

See optim for details and default.

parscale

See optim for details and default.

maxit

See optim for details. Default is 1999.

reltol

See optim for details and default.

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.

Value

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

Author(s)

Dylan Molenaar [e-mail]

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)