QQdiff {diffIRT} | R Documentation |
This function plots the observed response times against the predicted response times for a diffIRT
object.
QQdiff(object, items, plot=2, breaks=15, quant=NULL, maxRT=NULL)
object |
A |
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 |
quant |
The number of quantiles to be used. If |
maxRT |
The maximum response time used in finding the quantiles of the theoretical distribution. If |
QQdiff
calculates the predicted quantiles in the marginal response time distribution of the given
model (D-diffusion or Q-diffusion).
Function QQdiff
returns a list with entries:
qexp |
a vector with predicted quantiles. |
qobs |
a vector with observed quantiles. |
Dylan Molenaar d.molenaar@uva.nl
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.
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.
## 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)