# bdw.multicore: Producing several chains from a MCMC object of class 'bdw' In BDWreg: Bayesian Inference for Discrete Weibull Regression

## Description

This function is equipped with multicore options to produce several chains from a MCMC of class 'bdw'

## Usage

 `1` ```bdw.mc(dw.object, n.repeat = 10, cores = 0) ```

## Arguments

 `dw.object` Object of class 'bdw'. `n.repeat` The number of chains to be generated. `cores` The number of processors. If set to zero then the procedure uses all cores.

## Value

An object of class 'bdw'. All chains are combined into a list that is stored in an object named 'all'. The output of this function can be passed to plot() and summary().

## Author(s)

`bdw`, `plot.bdw`, `summary.bdw`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40``` ```## Not run: #==== multicore example - estimating logit-DW(regQ,B) parameters using RJ and 5 chains ====== #==== Two variables and four coefficients, including intercepts, are simulated and analysed set.seed(1234) n = 500 x1 = runif(n = n, min = 0, max = 1.5) x2 = runif(n = n, min = 0, max = 1.5) theta0 = .6 #<<< true parameter theta1 = 0 #<<< true parameter theta2 = .34 #<<< true parameter lq = theta0 + x1*theta1 + x2*theta2 q = exp(lq - log(1+exp(lq)) ) beta = 1.5 y = c() for(i in 1:n){ y[i] = BDWreg:::rdw(1,q = q[i],beta = beta) } data = data.frame(x1,x2,y) # <<<- data result = bdw(data = data , formula = y~. , RJ = TRUE , initial = rep(.5,4) , iteration = 25000 , reg.b = FALSE,reg.q = TRUE, v.scale = .1 , q.par = c(0,1) , b.par = c(0,1) , dist.q = dnorm , dist.b = dnorm ) result2 = bdw.mc(result,5) # <<<- multicore plot(result2) summary(result2) ## End(Not run) ```