scudoModel: Generate model for 'caret::train'

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/scudoModel.R

Description

This function generates a suitable input for the method argument of the function train from the package caret, that can be used to perform automatic parameter tuning (e.g. using cross-validation).

Usage

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scudoModel(nTop, nBottom, N, maxDist = 1, weighted = TRUE,
    complete = FALSE, beta = 1, distFun = NULL)

Arguments

nTop

number of up-regulated features to include in the signatures

nBottom

number of down-regulated features to include in the signatures

N

a number between 0 and 1, representing the fraction of the signature-to-signature distances that will be used to draw the graph

maxDist

an integer. Only nodes with a distance from a testing node less or equal to maxDist are used to perform the classification

weighted

logical, whether to consider the distances associated to the edges to compute the scores for the classification

complete

logical, whether to consider all the nodes in the training set to perform the classification. If TRUE, the arguments N, maxDist, weighted and beta are ignored

beta

a coefficient used to down-weight the influence of distant nodes on the classification outcome

distFun

the function used to compute the distance between two samples. See Details for the specification of this function

Details

This function can be used in conjunction with the functions in the package caret to tune the parameters of scudoClassify. The input of this function are vector of parameter values that the tuning procedure should explore. All possible combination of parameter values are explored by default. The user can change this using the search argument of the trainControl function.

The output of this function is a list that represens a classification model using scudoClassify and that can be used as input for the method argument of the function train.

Value

A named list

Author(s)

Matteo Ciciani matteo.ciciani@gmail.com, Thomas Cantore cantorethomas@gmail.com

See Also

scudoClassify, train, trainControl

Examples

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# Generate example dataset
expData <- data.frame(a = 1:10, b = 2:11, c = 10:1, d = 11:2,
    e = c(1:4, 10:5), f = c(7:10, 6:1), g = c(8:4, 1:3, 10, 9),
    h = c(6:10, 5:1), i = c(5:1, 6:10))
rownames(expData) <- letters[1:10]
groups <- factor(c(1,1,1,2,2,2,1,1,1))

# Run bootstrap. Notice that the dataset is transposed
ctrl <- caret::trainControl(method = "boot", number = 5)
model <- scudoModel(nTop = 3:5, nBottom = 3:5, N = 0.5, complete = TRUE)
set.seed(1)
bootRes <- caret::train(x = t(expData), y = groups, method = model,
    trControl = ctrl)

Matteo-Ciciani/scudo documentation built on May 24, 2020, 5:34 a.m.