buildRSM: Build Response Surface Model
In bartzbeielstein/SPOT: Sequential Parameter Optimization Toolbox

Description Usage Arguments Value See Also Examples

Using the rsm package, this function builds a linear response surface model.

1	buildRSM(x, y, control = list())

x

design matrix (sample locations), rows for each sample, columns for each variable.

y

vector of observations at x

control

(list), with the options for the model building procedure:

mainEffectsOnly: Logical, defaults to FALSE. Set to TRUE if a model with main effects only is desired (no interactions, second order effects).
canonical: Logical, defaults to FALSE. If this is TRUE, use the canonical path to descent from saddle points. Else, simply use steepest descent

returns an object of class spotRSM.

predict.spotRSM

## Create a test function: branin
braninFunction <- function (x) {	
	(x[2]  - 5.1/(4 * pi^2) * (x[1] ^2) + 5/pi * x[1]  - 6)^2 + 
10 * (1 - 1/(8 * pi)) * cos(x[1] ) + 10
}
## Create design points
x <- cbind(runif(20)*15-5,runif(20)*15)
## Compute observations at design points
y <- as.matrix(apply(x,1,braninFunction))
## Create model with default settings
fit <- buildRSM(x,y)
## Predict new point
predict(fit,cbind(1,2))
## True value at location
braninFunction(c(1,2))
## plots
plot(fit)
## path of steepest descent
descentSpotRSM(fit)