rjpdmp (version 2.0.0)

gen_sample: Generate samples for PDMP trajectory

Description

Get samples from PDMP trajectories taking a fixed time discretisation.

Usage

gen_sample(positions, times, nsample, theta = NULL, burn = 1)

Arguments

positions

Matrix of positions from the PDMP trajectory, each column should correspond to a position

times

Vector of event times from the PDMP trajectory

nsample

Number of desired samples from the PDMP trajectory

theta

Optional Matrix of velocities from the PDMP trajectory, each column should correspond to a velocity

burn

Index to start the discretisation from. Default is 1.

Value

Returns a list with the following objects:

x: Matrix of extracted samples of the position (x) taken using a fixed time discretisation of the PDMP

theta: Matrix of extracted samples of the velocity (theta) taken using a fixed time discretisation of the PDMP

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 {
samples <- gen_sample(zigzag_fit$positions, zigzag_fit$times, 10^4)

plot(zigzag_fit$positions[1,],zigzag_fit$positions[2,], type = 'l', xlab = 'x1', ylab = 'x2')
points(samples$xx[1,], samples$xx[2,], col='red', pch=20)
# }
# NOT RUN {
# }