Meta Model Interface: Multivariate Adaptive Regression Spline

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Description

Prediction based on earth package, using Multivariate Adaptive Regression Spline models Can be used both for single and multi objective SPOT.

Usage

1
spotPredictEarth(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

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.

Details

This is a model that can incorporate parameters which are marked as FACTORS (i.e. categorical parameters) in the region of interest, see spotROI. Please note that the design used to train the MARS model should contain all levels of the factor variable. FACTORS are not ordered, and therefore are impossible to extrapolate on. If new data is given in the design variable which contains unseen FACTOR levels, please note that this will probably create NA values in the prediction. (With the exception that no NA values are created if the concerned FACTOR is not selected as a predictor by earth) NA values might yield errors in your SPOT run, ending it prematurely. It is therefore recommended to build a initial design which contains at least one example of each FACTOR level.

Value

returns the list spotConfig with two new entries:
spotConfig$seq.modelFit fit of the earth model used with predict()
spotConfig$seq.largeDesignY the y values of the design, evaluated with the fit

See Also

spotPredictLmFactor

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