IndepMH {bayesdistreg} R Documentation

## Independence Metropolis-Hastings Algorithm

### Description

IndepMH computes random draws of parameters using a specified proposal distribution.

### Usage

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


### Arguments

 data data required for the posterior distribution propob a list of mean and variance-covariance of the normal proposal distribution (default:NULL) 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 (default: 1500) burn burn-in period for the MH algorithm (default: 500) 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 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 pardraws and the acceptance rate

### Examples

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



[Package bayesdistreg version 0.1.0 Index]