marginal_mean: Calculate the marginal mean

In rjpdmp: Reversible Jump PDMP Samplers

Calculate the marginal mean

Description

Calculate the marginal mean

Usage

marginal_mean(times, positions, thetas, marginals = NULL, burnin = 1)

Arguments

times	Vector of event times from the PDMP trajectory
positions	Matrix of positions from the PDMP trajectory, each column should correspond to a position
thetas	Matrix of PDMP velocities
marginals	Vector of indices to calculate the marginal means.
burnin	Number of events to use as burnin

Value

Returns the posterior mean of the parameter estimated using the PDMP trajectories.

Examples

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: 
b <- marginal_mean(zigzag_fit$times, zigzag_fit$positions, zigzag_fit$theta, marginals=1:p)

## End(Not run)

rjpdmp documentation built on March 18, 2022, 7:52 p.m.