nullDistDoublyTestedEdges {ppiStats} | R Documentation

## Null distribution of number of reciprocated, unreciprocated and missing edges in stochastic model.

### Description

Calculate the null distribution of the number of reciprocated, unreciprocated and missing edges in a stochastic model where each edge is tested twice.

### Usage

    nullDistDoublyTestedEdges(deltaMax, n, pFP, pFN)

### Arguments

`deltaMax` | Integer. Distributions will be calculated for model parameter delta`=0, 1, 2, ..., deltaMax`.
`n` | Integer. The parameter n of the model.
`pFP` | Numeric. The parameter pFP of the model.
`pFN` | Numeric. The parameter pFN of the model.

### Details

The model is described in the vignette Stochastic and systematic errors in PPI data, by looking at unreciprocated in- or out-edges by W. Huber, T. Chiang and R. Gentleman.

This function can be quite slow, its runtime grows quickly with `deltaMax` (and is roughly independent of `n`, `pFP`, `pFN`). The example below should take only a few seconds on a reasonable computer, though.

### Value

3d array with dimensions `nMax+1` x `nMax+1` x `deltaMax+1` whose element `p[nr+1, nu+1, delta+1]` is the corresponding joint probability. `nMax+1` is calculated (probably too conservatively) by the function to make sure that no probability leaks out of the array.

### Author(s)

Wolfgang Huber http://www.ebi.ac.uk/huber

### Examples

    p = nullDistDoublyTestedEdges(32, 1000, pFP=0.001, pFN=0.15)

    if(interactive() && require("RColorBrewer"))
      for(k in 1:dim(p)[3]) {
        image(sqrt(p[,,k]), xlab=expression(N[rec]), ylab=expression(N[unrec]),
            main = expression(P(N[rec], N[unrec]~";"~ delta^"*", n, p[FP], p[FN])),
            x = 1:dim(p)[1], y = 1:dim(p)[2],
            col = colorRampPalette(brewer.pal(9, "GnBu"))(256))
        text(35, 35, paste("delta", k, sep="="))
      }

[Package ppiStats version 1.6.0 Index]