Meta Model Interface: Multi Criteria Modelling

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Description

This interface function is supposed to be used for Multi Criteria Problems. It can be employed when the user wants to specify different models for each of the objectives, instead of modelling all the objectives with the same technique. The user has therefore to specify a list of configurations, where the different models and their settings are specified.

Usage

1
spotPredictMCO(rawB, mergedB, design, spotConfig, fit = NULL)

Arguments

rawB

unmerged data

mergedB

merged data

design

new design points which should be predicted

spotConfig

global list of all options, needed to provide data for calling functions. The most important elements in this list are here:
spotConfig$mco.configs list of model configurations, e.g. =list(list(seq.predictionModel.func="spotPredictForrester",seq.forr.lambda.upval=-5),list(seq.predictionModel.func="spotPredictForrester",seq.forr.lambda.upval=-1))
In this example, two Kriging models are specified for each of two objectives, but with different settings for the lower boundary of lambda. Else, different models could be specified, e.g., =list(list(seq.predictionModel.func="spotPredictForrester"),list(seq.predictionModel.func="spotPredictLm"))

fit

if an existing model fit is supplied, the model will not be build based on data, but only evaluated with the model fit (on the design data). To build the model, this parameter has to be NULL. If it is not NULL the parameters mergedB and rawB will not be used at all in the function.

Value

returns the list spotConfig with two new entries:
spotConfig$seq.modelFit fit of the model used with the predictor functions
spotConfig$seq.largeDesignY the y values of the design, evaluated with the fit

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