I have a set of 5 data points (x=10,20,30,40,50 and its corresponding response values y and noise as s.d. of y). These data are obtained from stochastic computer experiments.
How can I use DiceKriging in R to fit a kriging model for these data?
x <- seq(from=10, to=50, length=5)
y <- c(-0.071476,0.17683,0.19758,0.2642,0.4962)
noise <- c(0.009725,0.01432,0.03284, 0.1038, 0.1887)
Examples online with heterogeneous noise are pre-specified with coef.var, coef.trend and coef.theta. It is unlikely that I can have a priori on these.
I have referred to the answer here. However, other references suggest adding the nugget parameter lambda is similar to adding homogeneous noise, which is not likely "individual errors".
The use of km with noise is quite simple:
model <- km(~1, data.frame(x=x), y, noise.var = noise, covtype = "matern3_2")
However, your noise term make the line search part of L-BFGS algorithm fail. It may be due to the fact that is is strongly correlated with y, because when I run the following lines, it works:
noice <- c(0.009725,0.01432,0.03284, 0.001, 0.1887)
model <- km(~1, data.frame(x=x), y, noise.var = noise, covtype = "matern3_2")