hbmem (version 0.2)

dpsdSample: Function to fit hierarchical DPSD model to data.

Description

Runs MCMC estimation for the hierarchical DPSD model.

Usage

dpsdSample(dat, M = 5000, keep = (M/10):M, getDIC = TRUE,
freeCrit=TRUE,Hier=TRUE, jump=.01)

Arguments

dat

Data frame that must include variables Scond,cond,sub,item,lag,resp. Scond indexes studied/new, whereas cond indexes conditions nested within the studied or new conditions. Indexes for Scond,cond, sub, item, and respone must start at zer

M

Number of MCMC iterations.

keep

Which MCMC iterations should be included in estimates and returned. Use keep to both get ride of burn-in, and thin chains if necessary

getDIC

Logical. Should the function compute DIC value? This takes a while if M is large.

freeCrit

Logical. If true then criteria are estimated separately for each participant. Should be set to false if analizing only one participant (e.g., if averaging over subjects).

Hier

Logical. If true then the variances of effects (e.g., item effects) are estimated from the data, i.e., effects are treated as random. If false then these variances are fixed to 2.0 (.5 for recollection effects), thus treating these effects as

jump

The criteria and decorrelating steps utilize Matropolis-Hastings sampling routines, which require tuning. All MCMC functions should self-tune during the burnin period (iterations before keep), and they will alert you to the success of tuning.

Value

• The function returns an internally defined "uvsd" structure that includes the following components
• mu
  Indexes which element of blocks contain mu
• alpha
  Indexes which element of blocks contain participant effects, alpha
• beta
  Indexes which element of blocks contain item effects, beta
• s2alpha
  Indexes which element of blocks contain variance of participant effects (alpha).
• s2beta
  Indexes which element of blocks contain variance of item effects (beta).
• theta
  Indexes which element of blocks contain theta, the slope of the lag effect
• estN
  Posterior means of block parameters for new-item means
• estS
  Posterior means of block parameters for studied-item means
• estR
  Posterior means of block for Recollection means.
• estCrit
  Posterior means of criteria
• blockN
  Each iteration for each parameter in the new-item mean block. Rows index iteration, columns index parameter.
• blockS
  Same as blockN, but for the studied-item means
• blockR
  Same as blockN, but for the recollection-parameter means.
• s.crit
  Samples of each criteria.
• pD
  Number of effective parameters used in DIC. Note that this should be smaller than the actual number of parameters, as constraint from the hierarchical structure decreases the number of effective parameters.
• DIC
  DIC value. Smaller values indicate better fits. Note that DIC is notably biased toward complexity.
• M
  Number of MCMC iterations run
• keep
  MCMC iterations that were used for estimation and returned
• b0
  Metropolis-Hastings acceptance rates for decorrelating steps. These should be between .2 and .6. If they are not, the M, keep, or jump arguments need to be adjusted.
• b0Crit
  acceptance rates for criteria.

References

See Pratte, Rouder, & Morey (2009)

See Also

hbmem

Examples

#In this example we generate data from EVSD, then fit it with both
#hierarchical DPSD and DPSD assuming no participant or item effects.
library(hbmem)
sim=dpsdSim(I=30,J=200)
dat=as.data.frame(cbind(sim@subj,sim@item,sim@cond,sim@Scond,sim@lag,sim@resp))
colnames(dat)=c("sub","item","cond","Scond","lag","resp")
dat$lag[dat$Scond==1]=dat$lag[dat$Scond==1]-mean(dat$lag[dat$Scond==1])

M=200 #Too low for real analysis!
keep=50:M
DPSD=dpsdSample(dat,M=M)

#Look at all parameters
par(mfrow=c(3,3),pch=19,pty='s')

matplot(DPSD@blockN[,DPSD@muN],t='l',
ylab="muN")
abline(h=sim@muN,col="blue")
plot(DPSD@estN[DPSD@alphaN]~sim@alphaN)
abline(0,1,col="blue")
plot(DPSD@estN[DPSD@betaN]~sim@betaN)
abline(0,1,col="blue")

matplot(DPSD@blockS[,DPSD@muS],t='l',
ylab="muS")
abline(h=sim@muS,col="blue")
plot(DPSD@estS[DPSD@alphaS]~sim@alphaS)
abline(0,1,col="blue")
plot(DPSD@estS[DPSD@betaS]~sim@betaS)
abline(0,1,col="blue")

matplot(pnorm(DPSD@blockR[,DPSD@muS]),t='l',
ylab="P(recollection)")
abline(h=pnorm(sim@muR),col="blue")
plot(DPSD@estR[DPSD@alphaS]~sim@alphaR)
abline(0,1,col="blue")
plot(DPSD@estR[DPSD@betaS]~sim@betaR)
abline(0,1,col="blue")