dpsdSim: Function dpsdSim

In hbmem: Hierarchical Bayesian Analysis of Recognition Memory

Function dpsdSim

Description

Simulates data from a hierarchical DPSD model.

Usage

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)))

Arguments

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.

Value

The function returns an internally defined "dpsdSim" structure.

Author(s)

Michael S. Pratte

References

See Pratte, Rouder, & Morey (2009)

See Also

hbmem

Examples

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)

hbmem documentation built on Aug. 23, 2023, 1:10 a.m.