data.discretize: Function to discretize data based on user specified cutoffs

In predictionet: Inference for predictive networks designed for (but not limited to) genomic data

Description

This function enable discretization of data based on cutoffs specified by the users

Usage

data.discretize(data, cuts)

Arguments

data	matrix of continuous or categorical values (gene expressions for example); observations in rows, features in columns.
cuts	list of cutoffs for each variable.

Details

This function is discretizing the continuous value in data using the cutoffs specified in cuts to create categories represented by increasing integers in 1,2,...n where n is the maximum number of categories in the dataset.

Value

a matrix of categorical values where categories are {1,2,..,n} depending on the list of cutoffs specified in cuts; observations in rows, features in columns.

Author(s)

Benjamin Haibe-Kains

See Also

discretize

Examples

## load gene expression data for colon cancer data, list of genes related to RAS signaling pathway and the corresponding priors
data(expO.colon.ras)
## discretize the data in 3 categories
categories <- rep(3, ncol(data.ras))
## estimate the cutoffs (tertiles) for each gene
cuts.discr <- lapply(apply(rbind("nbcat"=categories, data.ras), 2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0, 1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]]) })
data.ras.bin <- data.discretize(data=data.ras, cuts=cuts.discr)

predictionet documentation built on Nov. 8, 2020, 7:48 p.m.