Nested variable selection using LOOCV

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Description

Nested variable selection using LOOCV

Usage

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svmFeatureSelectionLOOCV(obj, selectionMode="direct", alpha=0.1, p.value.adjust.method="none",
    test.type="mc-x2", mc.replicates=5000, cost.range=logseq(0.01,
    1e+05, 8), gamma.range=logseq(1e-05, 100, 8), max.prop.SV=0.9,
    kernel="radial", skip.DDGraph=FALSE)

Arguments

obj

the DDDataSet object

selectionMode

which variables to take, possible values: "direct" (alias "p"), "direct and joint" (alias "ps"), "joint if no direct" (alias "snp")

alpha

the alpha cutoff to use

p.value.adjust.method

the p value adjustment for multiple testing to be applied

test.type

the type of conditional independence test to be used

mc.replicates

the number of Monte-Carlo replicates when determining p values

cost.range

the range of cost parameter values to evaluate

gamma.range

the range of gamma parameter values to evaluate

max.prop.SV

the maximal proportion of support vectors to number of data points (rows in d)

kernel

kernel type to use (takes valid package e1071 names like "radial")

skip.DDGraph

if to skip DDGraph-based variable selection

Details

A function to select variables in nested way using the following algorithm:

  1. repeat for each row in dataset:

    1. make new DDDataSet by removing one row and apply DDGraphs to select features

    2. select best parameters using recalculateSVMparams (i.e. in an inner LOOCV loop)

    3. make the classifier with best parameters and calculate output on the unseen row (removed in step 1)

  2. return the collected predictions from step 1.3

Value

the predictions for class labels from LOOCV

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