cond_mean: Calculate the mean conditioned on being in a specific model

In rjpdmp: Reversible Jump PDMP Samplers

Calculate the mean conditioned on being in a specific model

Description

Calculate the mean conditioned on being in a specific model

Usage

cond_mean(times, positions, thetas, theta_c, 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
theta_c	Vector indicating the model to condition on, 1s for active variables and zeros for inactive variables
burnin	Number of events to use as burnin

Value

Returns the mean conditioned on being in model theta_c 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 <- cond_mean(zigzag_fit$times, zigzag_fit$positions, zigzag_fit$theta, theta_c = c(1,rep(0,p-1)))

## End(Not run)

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