1d:T7ce,# Simulate GP values from block-addtive kernel Simulate Gaussian process values from a given block-additive kernel ```r simAdditive(newdata, mu, parameter, covtype, cl, iso = FALSE, eps.R = 1e-08) ``` ## Arguments - `newdata`: matrix containing the points where to perform simulations - `mu`: trend parameter - `parameter`: list of size of 'cl' containing for each clique a list of parameters alpha (single value) and theta (numeric vector of values) - `covtype`: character string specifying the covariance structure to be used, to be chosen between "gauss", "matern5_2", "matern3_2", "exp" or "powexp" (see DiceKriging) - `cl`: list of cliques - `iso`: boolean vector indicating for each clique if it is isotropic (TRUE) or anisotropic (FALSE), defaults to iso = FALSE (all cliques anisotropic) - `eps.R`: small positive number indicating the nugget effect added to the covariance matrix diagonalk, defaults to eps.R = 1e-08 ## Returns a vector containing the simulated values ## References Muehlenstaedt, T.; Roustant, O.; Carraro, L.; Kuhnt, S. (2011) Data-driven Kriging models based on FANOVA-decomposition, **Statistics and Computing**. Rasmussen, C. E.; Williams, C. K. I. (2006), **Gaussian processes for machine learning**, MIT Press. ## Author(s) J. Fruth ## See Also `kmAdditive`, `simulate` ## Examples ```r ### 2 dimensional simulation x1 <- x2 <- seq(-1,1,,20) x <- expand.grid(x1,x2) covtype <- "matern3_2" mu <- 0 op <- par(no.readonly=TRUE); par(mfrow=c(1,2), mar=c(1,1,1,1)) # non-additive simulation parameter <- list(list(alpha=1, theta=c(0.8,0.8))) cl <- list(1:2) set.seed(1) y <- simAdditive(x, mu, parameter, covtype, cl) persp(x1,x2, matrix(y,20), theta=-40, col="lightblue", zlab="y") # additive simulation parameter <- list(list(alpha=0.5, theta=0.8), list(alpha=0.5, theta=0.8)) cl <- list(1,2) set.seed(1) y <- simAdditive(x, mu, parameter, covtype, cl) persp(x1,x2, matrix(y,20), theta=-40, col="lightblue", zlab="y") par(op) ```

simAdditive function