Predict Forrester Model (Re-interpolating)

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Description

Kriging predictor with re-interpolation to avoid stalling the optimization process which employs this model as a surrogate. This is supposed to be used with deterministic experiments, which do need a non-interpolating model that avoids predicting non-zero error at sample locations. This can be useful when the model is deterministic (i.e. repeated evaluations of one parameter vector do not yield different values) but does have a "noisy" structure (e.g. due to computational inaccuracies, systematical error).

Usage

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forrReintPredictor(x, ModelInfo, pred.all = FALSE)

Arguments

x

design matrix to be predicted

ModelInfo

fit of the Kriging model (settings and parameters)

pred.all

if TRUE return all (RMSE and prediction, in a dataframe), else return only prediction

Details

Please note that this re-interpolation implementation will not necessarily yield values of exactly zero at the sample locations used for model building. Slight deviations can occur.

Value

Returned value is dependent on the setting of pred.all
TRUE: data.frame with columns f (function values) and s (RMSE)
FALSE: vector of function values only

See Also

forrBuilder forrCoBuilder predict.forr

Examples

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## Create design points
x = cbind(runif(20)*15-5,runif(20)*15)
## Compute observations at design points (for Branin function)
y = as.matrix(apply(x,1,spotBraninFunction))
## Create model
fit = forrBuilder(x,y)
## first estimate error with regressive predictor
sreg = predict(fit,x,TRUE)$s
## now estimate error with re-interpolating predictor
sreint = forrReintPredictor(x,fit,TRUE)$s
print(sreg)
print(sreint)
## sreint should be close to zero, significantly smaller than sreg

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