simulate_process.kmBounded1D: Simulate responses vectors from a kmBounded model

In maatouk/constrKriging: Kriging with inequality constraints

Usage

## S3 method for class 'kmBounded1D'
simulate_process(object, nsim, seed = NULL, newdata)

Arguments

object	kmBounded model
nsim	the number of response vectors to simulate
seed	optional random seed
newdata	a vector which represents the points where to performs predictions

Examples

design = c(0, 0.1, 0.2, 0.42, 0.5, 0.9)
response = c(10, 7, -8, -5, 10, 15)
model = kmBounded1D(design, response, lower = -20, upper = 20, coef.cov=0.2, coef.var=100, basis.size = 50)
x = seq(0,1,,100)
y = simulate_process(object=model, newdata=x, nsim=100)
graphics::matplot(x, y, type='l', col='gray', lty = 1, ylab = "response", ylim=c(model$call$lower,model$call$upper))
lines(x, constrSpline(object=model)(x), lty=1, lwd=2, col='black')
lines(x, rowMeans(y), lty=2, lwd=2, col='black')
points(design, response, pch=19)
legend(0.45, -8, c("posterior mean", "posterior max"), 
       col = c('black', 'black'), text.col = "black",
       lty = c(2, 1), pch=c(NA_integer_, NA_integer_),lwd = c(2, 2), text.font=1,box.lty=0, cex=0.8)
abline(h=model$call$lower, lty=2)
abline(h=model$call$upper, lty=2)

maatouk/constrKriging documentation built on May 21, 2019, 10:50 a.m.