plot.bnemBs {bnem} | R Documentation

## Plot Bootstrap result

### Description

Shows the result of a Boostrap with either edge frequencies or confidence intervals

### Usage

    ## S3 method for class 'bnemBs'
    plot(
      x,
      scale = 3,
      shift = 0.1,
      cut = 0.5,
      dec = 2,
      ci = 0,
      cip = 0.95,
      method = "exact",
      ...
    )

### Arguments

`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

### Value

plot of the network from the bootstrap

### Author(s)

Martin Pirkl

### Examples

    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)

[Package bnem version 1.0.0 Index]