bivnormMH {Bolstad2} | R Documentation |
This function uses the MetropolisHastings algorithm to draw a sample from a correlated bivariate normal target density using a random walk candidate and an independent candidate density respectively where we are drawing both parameters in a single draw. It can also use the blockwise Metropolis Hastings algorithm and Gibbs sampling respectively to draw a sample from the correlated bivariate normal target.
bivnormMH(rho, rho1 = 0.9, sigma = c(1.2, 1.2), steps = 1000, type = 'ind')
rho |
the correlation coefficient for the bivariate normal |
rho1 |
the correlation of the candidate distribution. Only used when type = 'ind' |
sigma |
the standard deviations of the marginal distributions of the independent candidate density. Only used when type = 'ind' |
steps |
the number of Metropolis Hastings steps |
type |
the type of candidate generation to use. Can be one of 'rw' = random walk, 'ind' = independent normals, 'gibbs' = Gibbs sampling or 'block' = blockwise. It is sufficient to use 'r','i','g', or 'b' |
returns a list which contains a data frame called targetSample with members x and y. These are the samples from the target density.
## independent chain chain1.df<-bivnormMH(0.9)$targetSample ## random walk chain chain2.df<-bivnormMH(0.9, type = 'r')$targetSample ## blockwise MH chain chain3.df<-bivnormMH(0.9, type = 'b')$targetSample ## Gibbs sampling chain chain4.df<-bivnormMH(0.9, type = 'g')$targetSample oldPar <- par(mfrow=c(2,2)) plot(y ~ x, type = 'l', chain1.df, main = 'Independent') plot(y ~ x, type = 'l', chain2.df, main = 'Random Walk') plot(y ~ x, type = 'l', chain3.df, main = 'Blockwise') plot(y ~ x, type = 'l', chain4.df, main = 'Gibbs') par(oldPar)