hmflatloglin {bayess}

Metropolis-Hastings for the log-linear model under a flat prior

Description

This version of hmflatlogit operates on the log-linear model, assuming that the posterior associated with the flat prior and the data is well-defined. The proposal is based on a random walk Metropolis-Hastings step.

Usage

hmflatloglin(niter, y, X, scale)

Arguments

niter

number of iterations

y

binary response variable

X

matrix of covariates with the same number of rows as y

scale

scale of the Metropolis-Hastings random walk

Value

The function produces a sample of \beta's as a matrix of size niter x p, where p is the number of covariates.

See Also

hmflatlogit

Examples

airqual=na.omit(airquality)
ozone=cut(airqual$Ozone,c(min(airqual$Ozone),median(airqual$Ozone),max(airqual$Ozone)),
include.lowest=TRUE)
month=as.factor(airqual$Month)
tempe=cut(airqual$Temp,c(min(airqual$Temp),median(airqual$Temp),max(airqual$Temp)),
include.lowest=TRUE)
counts=table(ozone,tempe,month)
counts=as.vector(counts)
ozo=gl(2,1,20)
temp=gl(2,2,20)
mon=gl(5,4,20)
x1=rep(1,20)
lulu=rep(0,20)
x2=x3=x4=x5=x6=x7=x8=x9=lulu
x2[ozo==2]=x3[temp==2]=x4[mon==2]=x5[mon==3]=x6[mon==4]=1
x7[mon==5]=x8[ozo==2 & temp==2]=x9[ozo==2 & mon==2]=1
x10=x11=x12=x13=x14=x15=x16=lulu
x10[ozo==2 & mon==3]=x11[ozo==2 & mon==4]=x12[ozo==2 & mon==5]=1
x13[temp==2 & mon==2]=x14[temp==2 & mon==3]=x15[temp==2 & mon==4]=1
x16[temp==2 & mon==5]=1
X=cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16)
flatloglin=hmflatloglin(1000,counts,X,0.5)
par(mfrow=c(4,4),mar=1+c(1.5,1.5,1.5,1.5),cex=0.8)
for (i in 1:16) plot(flatloglin[,i],type="l",ylab="",xlab="Iterations")