rjpdmp (version 2.0.0)

cond_mean: 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

# 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 {
b <- cond_mean(zigzag_fit$times, zigzag_fit$positions, zigzag_fit$theta, theta_c = c(1,rep(0,p-1)))
# }
# NOT RUN {
# }