# A function to identify those proteins affected by either stochastic or systematic errors

### Description

This function takes in either a bait to prey Graph (matrix) and, based on a binomial error model, partitions proteins identified as either affected by systematic or stochastic error. It is a wrapper function that will eventually call the qbinom function.

### Usage

1 2 3 4 | ```
idSystematic(bpMat, viable, bpGraph = FALSE, pThresh = 0.01, pLevels =
1e-4, prob=0.5)
idStochastic(bpMat, bpGraph = FALSE, pThresh = 0.01, pLevels =
1e-4, prob=0.5)
``` |

### Arguments

`bpMat` |
Either a bait to prey directed graphNEL or its corresponding adjacency matrix. |

`viable` |
This is a character vector of viable proteins. It is only used in the idSystematic function. |

`bpGraph` |
A logical. If TRUE, than bpMat is passed in by the user as a graphNEL. |

`pThresh` |
The p-value threshold for which to partition stochastic or systematic errors |

`pLevels` |
A numeric. It gives the levels to calculate the countours of the function in p in the (n-in, n-out)-plane |

`prob` |
A numeric. The probability parameter in the call to the qbinom function. |

### Value

A character vector of proteins either affected by systematic or stochastic errors.

### Author(s)

T Chiang

### References

~put references to the literature/web site here ~

### Examples

1 2 | ```
library(ppiData)
idSystematic(Ito2001BPGraph, viableBaits[[1]], bpGraph=TRUE)
``` |