bayesdistreg (version 0.1.0)

RWMH: 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

# NOT RUN {
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))

# }