hmflatprobit {bayess} R Documentation

## Metropolis-Hastings for the probit model under a flat prior

### Description

This random walk Metropolis-Hastings algorithm takes advantage of the availability of the maximum likelihood estimator (available via the glm function) to center and scale the random walk in an efficient manner.

### Usage

hmflatprobit(niter, y, X, scale)


### Arguments

 niter number of iterations y binary response variable X covariates scale scale of the random walk

### Value

The function produces a sample of \beta's of size niter.

hmflatlogit

### Examples

data(bank)
bank=as.matrix(bank)
y=bank[,5]
X=bank[,1:4]
flatprobit=hmflatprobit(1000,y,X,1)
mean(flatprobit[101:1000,1])


[Package bayess version 1.4 Index]