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))