hbmem-package: Hierarchical Models of Recognition Memory

In hbmem: Hierarchical Bayesian Analysis of Recognition Memory

Hierarchical Models of Recognition Memory

Description

Contains functions for fitting hierarchical versions of EVSD, UVSD, DPSD, and our gamma signal detection model to recognition memory confidence-ratings data.

Author(s)

Michael S. Pratte <[e-mail]>

References

Morey, Pratte, and Rouder (2008); Pratte, Rouder, and Morey (2009); Pratte and Rouder (2012).

See Also

'uvsdSample' to fit hierarchical UVSD model, 'uvsdSim' to simulate data from the hierarchical UVSD model, 'dpsdSample' to fit the hierarchial DPSD model, 'dpsdSim' to simulate data from the hierarchial DPSD model, 'dpsdPosSim' and 'dpsdPosSample' for the DPSD model with positive sensitivity, and datasets from our publications.

Examples

#In this example data are simulated from EVSD
#They are then fit by both UVSD and DPSD

library(hbmem)
sim=uvsdSim(s2aS2=0,s2bS2=0) #Simulate data from hierarchical EVSD
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")

M=10 #Set way low for speed
keep=2:M
#For real analysis we run 105000 iterations
#with the first 5000 serving as burnin, and
#only keep every 10th iteration for analysis,
#i.e., thinning the chanins to mitgate autocorrelation.
evsd=uvsdSample(dat,M=M,keep=keep,equalVar=TRUE) #Fit EVSD
uvsd=uvsdSample(dat,M=M,keep=keep,freeSig2=TRUE) #Fit UVSD w/1 Sigma2
dpsd=dpsdSample(dat,M=M,keep=keep) #Fit DPSD 

#Look at available information
slotNames(uvsd)
slotNames(dpsd)

#Compare DIC; smaller is better
evsd@DIC
uvsd@DIC
dpsd@DIC

#Effective parameters.  Because there are no
#real effects on studied-item variance, the
#hierarchical models are drastically shrinking these
#effect parameters to zero, so that they do not
#count as full parameters.
evsd@pD
uvsd@pD
dpsd@pD

#PLOTS FROM UVSD FIT
par(mfrow=c(3,2),pch=19,pty='s')
#Make sure chains look OK
matplot(uvsd@blockN[,uvsd@muN],t='l',xlab="Iteration",ylab="Mu-N")
abline(h=sim@muN,col="blue")
matplot(uvsd@blockS[,uvsd@muS],t='l',xlab="Iteration",ylab="Mu-S")
abline(h=sim@muS,col="blue")

#Estimates of Alpha as function of true values
plot(uvsd@estN[uvsd@alphaN]~sim@alphaN,xlab="True
Alpha-N",ylab="Est. Alpha-N");abline(0,1,col="blue")
plot(uvsd@estS[uvsd@alphaS]~sim@alphaS,xlab="True
Alpha-S",ylab="Est. Alpha-S");abline(0,1,col="blue")
#Estimates of Beta as function of true values
plot(uvsd@estN[uvsd@betaN]~sim@betaN,xlab="True
Beta-N",ylab="Est. Beta-N");abline(0,1,col="blue")
plot(uvsd@estS[uvsd@betaS]~sim@betaS,xlab="True
Beta-S",ylab="Est. Beta-S");abline(0,1,col="blue")

###Look at Sigma2 and Recollection from UVSD and DPSD###
par(mfrow=c(2,3),pch=19,pty='s')
plot(sqrt(exp(uvsd@blockS2[,uvsd@muS])),
t='l',ylab="Sigma",main="Grand Mean")
abline(h=1,col="blue")
hist(uvsd@blockS2[,uvsd@s2alphaS],main="Participant Effect")
hist(uvsd@blockS2[,uvsd@s2betaS],main="Item Effect")

plot(pnorm(dpsd@blockR[,dpsd@muS]),
t='l',ylab="P(Recollection)",main="Grand Mean")
abline(h=0,col="blue")
hist(dpsd@blockR[,dpsd@s2alphaS],main="Participant Effect")
hist(dpsd@blockR[,dpsd@s2betaS],main="Item Effect")


#See what DPSD does with EVSD effects
par(mfrow=c(2,3))
plot(dpsd@estN[dpsd@alphaN]~sim@alphaN,xlab="True
Alpha-N",ylab="DPSD Alpha-N");abline(0,1,col="blue")
plot(dpsd@estS[dpsd@alphaS]~sim@alphaS,xlab="True
Alpha-S",ylab="DPSD Alpha-S");abline(0,1,col="blue")
plot(dpsd@estR[dpsd@alphaS]~sim@alphaS,xlab="True
Alpha-S",ylab="DPSD Alpha-R");abline(0,1,col="blue")

plot(dpsd@estN[dpsd@betaN]~sim@betaN,xlab="True
Beta-N",ylab="DPSD Beta-N");abline(0,1,col="blue")
plot(dpsd@estS[dpsd@betaS]~sim@betaS,xlab="True
Beta-S",ylab="DPSD Beta-S");abline(0,1,col="blue")
plot(dpsd@estR[dpsd@betaS]~sim@betaS,xlab="True
Beta-S",ylab="DPSD Beta-R");abline(0,1,col="blue")

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