nempibs: Bootstrapping function

In cbg-ethz/nempi: Inferring unobserved perturbations from gene expression data

Bootstrapping function

Description

Bootstrap algorithm to get a more stable result.

Usage

nempibs(D, bsruns = 100, bssize = 0.5, replace = TRUE, ...)

Arguments

D	either a binary effects matrix or log odds matrix as
bsruns	number of bootstraps
bssize	number of E-genes for each boostrap
replace	if TRUE, actual bootstrap, if False sub-sampling
...	additional parameters for the function nempi

Value

list with aggregate Gamma and aggregate causal network phi

Author(s)

Martin Pirkl

Examples

D <- matrix(rnorm(1000*100), 1000, 100)
colnames(D) <- sample(seq_len(5), 100, replace = TRUE)
Gamma <- matrix(sample(c(0,1), 5*100, replace = TRUE, p = c(0.9, 0.1)), 5,
100)
Gamma <- apply(Gamma, 2, function(x) return(x/sum(x)))
Gamma[is.na(Gamma)] <- 0
rownames(Gamma) <- seq_len(5)
result <- nempibs(D, bsruns = 3, Gamma = Gamma)

cbg-ethz/nempi documentation built on Nov. 5, 2024, 11:27 p.m.