dpsdSim {hbmem} | R Documentation |
Simulates data from a hierarchical DPSD model.
dpsdSim(NN=2,NS=1,I=30,J=200,K=6,muN=c(-.7,-.5),s2aN=.2,s2bN=.2, muS=0,s2aS=.2,s2bS=.2,muR=qnorm(.25),s2aR=.2,s2bR=.2, crit=matrix(rep(c(-1.6,-.5,0,.5,1.6),each=I),ncol=(K-1)))
NN |
Number of new-item conditions. |
NS |
Number of studied-item conditions. |
I |
Number of participants. |
J |
Number of items. |
K |
Number of response options. |
muN |
Mean of new-item distribution. If there are more than one new-item conditions this is a vector of means with length equal to NN. |
s2aN |
Variance of participant effects on mean of new-item distribution. |
s2bN |
Variance of item effects on mean of new-item distribution. |
muS |
Mean of studied-item distribution. If there are more than new-item conditions this is a vector of means with length equal to NNone studied-item conditions this is a vector of means with length equal to NS. |
s2aS |
Variance of participant effects on mean of studied-item distribution. |
s2bS |
Variance of item effects on mean of studied-item distribution. |
muR |
Mean recollection, on probit space. |
s2aR |
Variance of participant effects recollection. |
s2bR |
Variance of item effects on recollection. |
crit |
Matrix of criteria (not including -Inf or Inf). Columns correspond to criteria, rows correspond to participants. |
The function returns an internally defined "dpsdSim" structure.
Michael S. Pratte
See Pratte, Rouder, & Morey (2009)
hbmem
library(hbmem) #Data from hiererchial model sim=dpsdSim() slotNames(sim) #Scond indicates studied/new #cond indicates which condition (e.g., deep/shallow) table(sim@resp,sim@Scond,sim@cond) #Usefull to make data.frame for passing to functions dat=as.data.frame(cbind(sim@subj,sim@item,sim@Scond,sim@cond,sim@lag,sim@resp)) colnames(dat)=c("sub","item","Scond","cond","lag","resp") table(dat$resp,dat$Scond,dat$cond)