RWMH {bayesdistreg} R Documentation

## Random Walk Metropolis-Hastings Algorithm

### Description

RWMH computes random draws of parameters using a specified proposal distribution. The default is the normal distribution

### Usage

RWMH(data, propob = NULL, posterior = NULL, iter = 1500,
burn = 500, vscale = 1.5, start = NULL, prior = "Normal",
mu = 0, sig = 10)


### Arguments

 data data required for the posterior distribution. First column is the outcome propob a list of mean and variance-covariance of the normal proposal distribution (default: NULL i.e. internally generated) posterior the posterior distribution. It is set to null in order to use the logit posterior. The user can specify log posterior as a function of parameters and data (pars,data) iter number of random draws desired burn burn-in period for the Random Walk MH algorithm vscale a positive value to scale up or down the variance-covariance matrix in the proposal distribution start starting values of parameters for the MH algorithm. It is automatically generated from the proposal distribution but the user can also specify. prior the prior distribution (default: "Normal", alternative: "Uniform") mu the mean of the normal prior distribution (default:0) sig the variance of the normal prior distribution (default:10)

### Value

val a list of matrix of draws Matpram and the acceptance rate

### Examples

y = indicat(faithful$waiting,70) x = scale(cbind(faithful$eruptions,faithful$eruptions^2)) data = data.frame(y,x); propob<- lapl_aprx(y,x) RWMHob_n<- RWMH(data=data,propob,iter = 102, burn = 2) # prior="Normal" RWMHob_u<- RWMH(data=data,propob,prior="Uniform",iter = 102, burn = 2) par(mfrow=c(3,1));invisible(apply(RWMHob_n$Matpram,2,function(x)plot(density(x))))
invisible(apply(RWMHob_u\$Matpram,2,function(x)plot(density(x))));par(mfrow=c(1,1))



[Package bayesdistreg version 0.1.0 Index]