diffIRT (version 1.5)

QQdiff: Assessing diffIRT model fit for the response times using QQ-plots

Description

This function plots the observed response times against the predicted response times for a diffIRT object.

Usage

QQdiff(object, items, plot=2, breaks=15, quant=NULL, maxRT=NULL)

Arguments

object

A diffIRT object for which the QQ-plots need to be created.

items

A vector denoting for which items the QQ-plots need to be created.

plot

Integer; 1: only QQ-plots. 2: both a QQ-plot and a histogram containing the predicted and observed distribution.

breaks

Number of breaks to be used in hist when plot=2.

quant

The number of quantiles to be used. If NULL, the number of quantiles will equal the number of subjects.

maxRT

The maximum response time used in finding the quantiles of the theoretical distribution. If NULL, twice the maximum observed response time is used for each item. Increasing maxRT will increase computation time and should only be used when uniroot produces errors.

Value

Function QQdiff returns a list with entries:

qexp

a vector with predicted quantiles.

qobs

a vector with observed quantiles.

Details

QQdiff calculates the predicted quantiles in the marginal response time distribution of the given model (D-diffusion or Q-diffusion).

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. factest for estimation of factor scores (person drift rate and person boundary separation). simdiff for simulating data according to the D-diffusion or Q-diffusion IRT model.

Examples

## Not run: 
# # open rotation data
# data(rotation)
# x=rotation[,1:10]
# rt=rotation[,11:20]
#  
# # fit an unconstrained Q-diffusion model
# res1=diffIRT(rt,x,model="Q")           
# 
# # make QQ-plots and histograms for items 1 to 4.
# QQdiff(res1, items=1:4, plot=2, maxRT=rep(50,4))
# ## End(Not run)