plot.bnemBs {bnem} | R Documentation |
Shows the result of a Boostrap with either edge frequencies or confidence intervals
## S3 method for class 'bnemBs' plot( x, scale = 3, shift = 0.1, cut = 0.5, dec = 2, ci = 0, cip = 0.95, method = "exact", ... )
x |
bnemBs object |
scale |
numeric value for scaling the edgewidth |
shift |
numeric value for shifting the edgewidth |
cut |
shows only edges with a fraction larger than cut |
dec |
integer for function round |
ci |
if TRUE shows confidence intervals |
cip |
range for the confidence interval, e.g. 0.95 |
method |
method to use for conidence interval computation (see function binom.confint from package binom) |
... |
additional parameters for the function mnem::plotDnf |
plot of the network from the bootstrap
Martin Pirkl
sifMatrix <- rbind(c("A", 1, "B"), c("A", 1, "C"), c("B", 1, "D"), c("C", 1, "D")) temp.file <- tempfile(pattern="interaction",fileext=".sif") write.table(sifMatrix, file = temp.file, sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE) PKN <- CellNOptR::readSIF(temp.file) CNOlist <- dummyCNOlist("A", c("B","C","D"), maxStim = 1, maxInhibit = 2, signals = c("A", "B","C","D")) model <- CellNOptR::preprocessing(CNOlist, PKN, maxInputsPerGate = 100) expression <- matrix(rnorm(nrow(slot(CNOlist, "cues"))*10), 10, nrow(slot(CNOlist, "cues"))) fc <- computeFc(CNOlist, expression) initBstring <- rep(0, length(model$reacID)) res <- bnemBs(search = "greedy", model = model, CNOlist = CNOlist, fc = fc, pkn = PKN, stimuli = "A", inhibitors = c("B","C","D"), parallel = NULL, initBstring = initBstring, draw = FALSE, verbose = FALSE, maxSteps = Inf)