simulation.data.cor: Simulate gene expression data.

In silkeszy/Pomona: Identification of relevant variables in omics data sets using Random Forests

Simulate gene expression data.

Description

Generates simulated gene expression data in a regression setting.

Usage

simulation.data.cor(no.samples, group.size, no.var.total, null = FALSE)

Arguments

no.samples	number of samples.
group.size	number of variables in each of the six groups of correlated variables.
no.var.total	total number of variables.
null	simulate null model (using independent functional variables).

Details

The underlying simulation model is described in detail in the paper XXX. In brief, a nonlinear regression model based on three uniformly distributed variables is used. Predictor variables are simulated to be correlated with one of those functional variables. In addition, independent, uniformly distributed predictor variables are simulated.

Value

A data.frame with samples in rows and variables in columns (Note: first column contains simulated phenotype). Variables are named as y (= phenotype), g.i.j (= variable j in group i) and ind.k (= k-th independent variable).

Examples

# simulate toy data set
data = simulation.data.cor(no.samples = 100, group.size = rep(10, 6), no.var.total = 200)
                     

silkeszy/Pomona documentation built on March 31, 2022, 11:13 p.m.