hbmem (version 0.2)

sampleNormb: Function sampleNormb

Description

Same as sampleNorm, but assumes an additive model on sigma2, and takes the block of sigma2 parameters as argument

Usage

sampleNormb(sample,y,cond,subj,item,lag,N,I,J,R,ncond,nsub,nitem,s2mu,s2a,s2b,meta,metb,blockSigma2,sampLag=1,Hier=1)

Arguments

sample

Block of linear model parameters from previous iteration.

y

Vector of data

cond

Vector of condition index, starting at zero.

subj

Vector of subject index, starting at zero.

item

Vector of item index, starting at zero.

lag

Vector of lag index, zero-centered.

N

Number of conditions.

I

Number of subjects.

J

Number of items.

R

Total number of trials.

ncond

Vector of length (N) containing number of trials per each condition.

nsub

Vector of length (I) containing number of trials per each subject.

nitem

Vector of length (J) containing number of trials per each item.

s2mu

Prior variance on the grand mean mu; usually set to some large number.

s2a

Shape parameter of inverse gamma prior placed on effect variances.

s2b

Rate parameter of inverse gamma prior placed on effect variances. Setting both s2a AND s2b to be small (e.g., .01, .01) makes this an uninformative prior.

meta

Matrix of tuning parameter for metropolis-hastings decorrelating step on mu and alpha. This hould be adjusted so that .2 < b0 < .6.

metb

Tunning parameter for decorrelating step on alpha and beta.

blockSigma2

Block of parameters for Sigma2 (on log scale). Like all blocks, first element is the overall mean, followed by participant effects and then item effects.

sampLag

Logical. Whether or not to sample the lag effect.

Hier

Locial. If TRUE then effect variances are estimated from data. If false, then these values are fixed to whatever is in the s2alpha and s2beta slots of sample. This value should always be TRUE unless you know what you are doing.

Value

• The function returns a list. The first element of the list is the newly sampled block of parameters. The second element contains a vector of 0s or 1s indicating which of the decorrelating steps were accepted.

See Also

hbmem,sampleSig2b

Examples

library(hbmem)
N=2
I=50
J=200
B=N+I+J+3
R = I * J

mu=c(3,5)
muS2=log(c(1,2))
alpha = rnorm(I, 0, sqrt(.2))
beta = rnorm(J, 0, sqrt(.2))
alphaS2 = rnorm(I, 0, sqrt(.2))
betaS2 = rnorm(J, 0, sqrt(.2))
cond=sample(0:(N-1),R,replace=TRUE)
subj = rep(0:(I - 1), each = J)
item = rep(0:(J - 1), I)
lag = rep(0, R)
lag=runif(R,-500,500)
lag=lag-mean(lag)
resp = 1:R
for (r in 1:R) {
    mean = mu[cond[r] + 1] + alpha[subj[r] + 1] + beta[item[r] + 1]
    sd = sqrt(exp(muS2[cond[r]+1] + alphaS2[subj[r] + 1] +
betaS2[item[r] + 1] + .005*lag[r]))
    resp[r] = rnorm(1, mean, sd)
}
sim=(as.data.frame(cbind(cond,subj, item, lag, resp)))
attach(sim)
plot(resp~lag)

########MCMC SETUP##########
blockS=blockS2=matrix(0,nrow=1000,ncol=B)
blockS[,B-1]=blockS[,B-2]=blockS2[,B-1]=blockS2[,B-2]=.5
b0mean=c(0,0)
b0S2=rep(0,B)
met=rep(.01,B)
jump=.0001
ncond=table(cond)
nsub=table(subj)
nitem=table(item)

for(m in 2:1000) #way to low for real analysis
  {
    tmp=sampleNormb(blockS[m-1,],resp,cond,subj,item,lag,N,I,J,I*J,ncond,nsub,nitem,10,.01,.01,.02,.005,blockS2[m-1,],1,1)
    blockS[m,]=tmp[[1]]
    b0mean=b0mean+tmp[[2]]
    
    tmp=sampleSig2b(blockS2[m-1,],resp,cond,subj,item,lag,N,I,J,I*J,ncond,nsub,nitem,10,.01,.01,met,blockS[m,],1,1)
    blockS2[m,]=tmp[[1]]
    b0S2=b0S2+tmp[[2]]
   if(m<1000) met=met+(b0S2/m<.3)*-jump +(b0S2/m>.5)*jump
    met[met<jump]=jump
#met[B]=.0001
  }
b0mean/m
b0S2/m

s=colMeans(blockS)
s2=colMeans(blockS2)

par(mfrow=c(3,3))
matplot(blockS[,1:N],t='l')
abline(h=mu)
plot(s[(N+1):(I+N)]~alpha);abline(0,1)
plot(s[(I+N+1):(I+J+N)]~beta);abline(0,1)

matplot(blockS2[,1:N],t='l')
abline(h=muS2)
plot(s2[(N+1):(I+N)]~alphaS2);abline(0,1)
plot(s2[(I+N+1):(I+N+J)]~betaS2);abline(0,1)

plot(blockS2[,B-2],t='l')
plot(blockS2[,B-1],t='l')
plot(blockS2[,B],t='l')