brightness: A Simulated Response Time Dataset according to an...
In diffIRT: Diffusion IRT Models for Response and Response Time Data

The data are simulated according to a design similar as that of a real brightness discrimination experiment by Ratcliff & Rouder (1998). In this experiment, the subject had to decide for a number of trials whether the brightness of a stimulus (a randomly generated array of pixels displayed on a computer screen) was either 'high' or 'low'. The true brightness of the stimuli were manipulated into a number of levels and administered with a speed instruction ("respond as fast as possible") and with an accuracy instruction ("respond as accurate as possible"). Present dataset was simulated according to a design with 6 different brightness levels and 2 speed instructions resulting in 12 conditions. In the brightness data matrix, the first 12 columns are the responses and the next 12 columns are the response times. Each trial is assigned to a separate row with the response time of that trial in the corresponding column and NA's on the remaining columns. Similarly for the responses. In addition, the data are arranged in such a way that the first 6 conditions are the speed instructed stimuli and the next 6 conditions are the corresponding accuracy instructed versions of these stimuli. See below for an example how to analyse these data using the diffIRT package (taken from Molenaar, Tuerlinckx, & van der Maas, 2015).

Molenaar, D., Tuerlinkcx, F., & van der Maas, H.L.J. (2015). Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT. Journal of Statistical Software, 66(4), 1-34. URL http://www.jstatsoft.org/v66/i04/.

Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347-356.

data(brightness)
x=brightness[,1:12]
rt=brightness[,13:24]

## Not run: 
res = diffIRT(rt,x,model="D",constrain=c(rep(1,6),
rep(2,6),3:8,3:8,rep(9,12),0,10), start=c(rep(NA,36),0,NA))

## End(Not run)