exprso (version 0.5.1)

exprso: The exprso Package

Description

Welcome to the exprso package!

The exprso function imports data into the learning environment.

See mod to process the data.

See split to split off a test set.

See fs to select features.

See build to build models.

See pl to build models high-throughput.

See pipe to process pipelines.

See buildEnsemble to build ensembles.

See exprso-predict to deploy models.

See conjoin to merge objects.

Usage

exprso(x, y, label = 1, switch = FALSE)

Arguments

x

A matrix of feature data for all samples. Rows should contain samples and columns should contain features.

y

A vector of outcomes for all samples. If class(y) == "character" or class(y) == "factor", exprso prepares data for binary or multi-class classification. Else, exprso prepares data for regression. If y is a matrix, the program uses the column in label.

label

A numeric scalar or character string. The column to use as the label if y is a matrix.

switch

A logical scalar. Toggles which class label is called Control in binary classification.

Value

An ExprsArray object.

Examples

# NOT RUN {
library(exprso)
data(iris)
array <- exprso(iris[,1:4], iris[,5])
arrays <- splitSample(array, percent.include = 67)
array.train <- fsANOVA(arrays[[1]], top = 0)
array.train <- fsPrcomp(array.train, top = 3)
mach <- buildSVM(array.train, top = 5, kernel = "linear", cost = 1)
predict(mach, arrays[[2]])
# }