Metropolis-Hastings for the probit model under a noninformative prior

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Description

This function runs a Metropolis-Hastings algorithm that produces a sample from the posterior distribution for the probit model coefficient beta associated with a noninformative prior defined in Chapter 4.

Usage

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hmnoinfprobit(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 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

hmnoinflogit, hmflatprobit

Examples

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data(bank)
bank=as.matrix(bank)
y=bank[,5]
X=bank[,1:4]
noinfprobit=hmflatprobit(1000,y,X,1)
par(mfrow=c(1,3),mar=1+c(1.5,1.5,1.5,1.5))
plot(noinfprobit[,1],type="l",xlab="Iterations",ylab=expression(beta[1]))
hist(noinfprobit[101:1000,1],nclass=50,prob=TRUE,main="",xlab=expression(beta[1]))
acf(noinfprobit[101:1000,1],lag=10,main="",ylab="Autocorrelation",ci=FALSE)

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