nempibs {nempi} | R Documentation |
Bootstrap algorithm to get a more stable result.
nempibs(D, bsruns = 100, bssize = 0.5, replace = TRUE, ...)
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 |
list with aggregate Gamma and aggregate causal network phi
Martin Pirkl
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)