Question

Id	Neighborhood	NeiNumber	OverallQual	SalePrice
1	CollgCr	1	7	279500
2	CollgCr	1	7	208500
3	CollgCr	1	7	223500
4	CollgCr	1	8	230000
5	CollgCr	1	5	145000
6	BrkSide	2	5	118000
7	BrkSide	2	7	132000
8	Sawyer	3	4	90000
9	Sawyer	3	5	129500
10	Sawyer	3	5	144000
11	NridgHt	4	8	325300
12	NridgHt	4	8	256300
13	NridgHt	4	8	306000

I wish to make hierarchical bayesian model by R code but I can by find out how can I.

Answer 1

new<-read.csv("1242018.csv", header = TRUE, sep = ",")

install.packages("bayesm")

Z<-new$SalePrice

Z[,1]=rep(1,nrow(Z))
Z[,2]=Z[,2]-mean(Z[,2])
Z[,3]=Z[,3]-mean(Z[,3])
Z[,4]=Z[,4]-mean(Z[,4])
Z=as.matrix(Z)
hh=levels(factor(choiceAtt$id))
nhh=length(hh)
lgtdata=NULL
for (i in 1:nhh) {
y=choiceAtt[choiceAtt[,1]==hh[i],2]
nobs=length(y)
X=as.matrix(choiceAtt[choiceAtt[,1]==hh[i],c(3:16)])
lgtdata[[i]]=list(y=y,X=X)
}
cat("Finished Reading data",fill=TRUE)
fsh()
Data=list(lgtdata=lgtdata,Z=Z)
Mcmc=list(R=20000,sbeta=0.2,keep=20)
out=rhierBinLogit(Data=Data,Mcmc=Mcmc)