rjpdmp (version 2.0.0)

plot_pdmp_multiple: Plot multiple PDMP dynamics and MCMC samples for posterior distributions

Description

Plots to compare PDMP samplers and optionally MCMC samples.

Usage

plot_pdmp_multiple(
  list_pdmp,
  coords = 1:2,
  inds = 1:10^3,
  nsamples = 10^3,
  burn = 0.1,
  mcmc_samples = NULL,
  pch = 20,
  cols = NULL
)

Arguments

list_pdmp

List of PDMP sampler trajectories to plot

coords

Vector of coordinates to plot the marginal and joint distributions

inds

Vector of indices of the PDMP trajectories to plot.

nsamples

Number of samples to generate and use for marginal density estimates of the PDMP methods

burn

Percentage of events to use as burn-in. Should be between 0 and 1, default 0.1.

mcmc_samples

Optional Matrix of samples from an MCMC method. Each row should be a sample.

pch

The graphics parameter for off diagonal plots. Default is 20.

cols

Colours to be used for plotting the PDMPs and MCMC samples (in order).

Value

Generates a plot of the marginal density on the diagonal and pairs plots of the trajectories

Examples

# NOT RUN {
generate.logistic.data <- function(beta, n.obs, Sig) {
p <- length(beta)
dataX <- MASS::mvrnorm(n=n.obs,mu=rep(0,p),Sigma=Sig)
vals <- dataX %*% as.vector(beta)
generateY <- function(p) { rbinom(1, 1, p)}
dataY <- sapply(1/(1 + exp(-vals)), generateY)
return(list(dataX = dataX, dataY = dataY))
}

n <- 15
p <- 25
beta <- c(1, rep(0, p-1))
Siginv <- diag(1,p,p)
Siginv[1,2] <- Siginv[2,1] <- 0.9
set.seed(1)
data <- generate.logistic.data(beta, n, solve(Siginv))
ppi <- 2/p

zigzag_fit <- zigzag_logit(maxTime = 1, dataX = data$dataX,datay =data$dataY,
                           prior_sigma2 = 10,theta0 = rep(0, p),
                           x0 = rep(0, p), rj_val = 0.6,
                           ppi = ppi)
# }
# NOT RUN {
bps_fit <- bps_n_logit(maxTime = 1, dataX = data$dataX, datay = data$dataY,
                       prior_sigma2 = 10, theta0 = rep(0, p), x0 = rep(0, p),
                       ref = 0.1, rj_val = 0.6,ppi = ppi)

plot_pdmp_multiple(list(zz=zigzag_fit,bps=bps_fit), coords = 1:2, inds = 1:10^3,
                      nsamples = 1e4, burn = .1)
# }
# NOT RUN {
# }