View source: R/hmnoinfloglin.R
hmnoinfloglin | R Documentation |
This function is a version of hmnoinflogit
for the log-linear model, using a non-informative
prior defined in Chapter 4 and a proposal based on a random walk Metropolis-Hastings step.
hmnoinfloglin(niter, y, X, scale)
niter |
number of iterations |
y |
binary response variable |
X |
matrix of covariates with the same number of rows as |
scale |
scale of the random walk |
The function produces a sample of beta's as a matrix of size niter
x p
,
where p
is the number of covariates.
hmflatloglin
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]=1 x6[mon==4]=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]=x13[temp==2 & mon==2]=1 x14[temp==2 & mon==3]=x15[temp==2 & mon==4]=x16[temp==2 & mon==5]=1 X=cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16) noinloglin=hmnoinfloglin(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(noinloglin[,i],type="l",ylab="",xlab="Iterations")
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