DiceKriging (version 1.6.0)

trend.deltax: Trend derivatives

Description

Computes the gradient of the vector of trend basis functions f(x)=(f1(x);...;fp(x))

Usage

trend.deltax(x, model, h = sqrt(.Machine$double.eps))

Arguments

x

a vector representing the specific location.

model

an object of class km.

h

the precision for numerical derivatives.

Value

A pxd matrix where the p rows contain the gradient of the trend basis functions.

See Also

covVector.dx

Examples

# NOT RUN {
X <- expand.grid(x1=seq(0,1,length=4), x2=seq(0,1,length=4), x3=seq(0,1,length=4))
fun <- function(x){
  (x[1]+2*x[2]+3*x[3])^2
}
y <- apply(X, 1, fun) 

x <- c(0.2, 0.4, 0.6)
coef.cov=c(0.5, 0.9, 1.3); coef.var=3

m <- km(~.^2, design=X, response=y, coef.cov=coef.cov, coef.var=coef.var)
grad.trend <- trend.deltax(x, m)
print(grad.trend)

m <- km(~. + I(x1^2) + I(x2^2) + I(x3^2), 
        design=X, response=y, coef.cov=coef.cov, coef.var=coef.var)
grad.trend <- trend.deltax(x, m)
print(grad.trend)
# }