Description Usage Arguments Details Value Author(s) References See Also Examples

Maximum a posteriori (MAP) estimate via integer linear programming (ILP).

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`I` |
The incidence 0-1 matrix with unique row and column names, where rows are parts (genes) and columns are wholes (gene-sets). |

`y` |
Gene-level 0-1 data with the same names as the row names of I. |

`alpha` |
The false positive rate in role model, numeric value between 0 and 1. See reference. |

`gamma` |
The true positive rate in role model, numeric value between 0 and 1. See reference. |

`p` |
The prior active probability of wholes in role model, numeric value between 0 and 1. See reference. |

R package `Rglpk`

is used to perform the integer linear programming. Generally, alpha and gamma can be estimated from the gene-level data by users themselves (see reference for examples), and alpha is less than gamma. p can be estimated via R package `MGSA`

with alpha and gamma fixed. Since ILP is a complex problem in the optimization field, the running time might be very long. This function is invoked in `sequentialRM`

.

The output has the same structure as `Rglpk_solve_LP`

in the `Rglpk`

package, which is a list consisting of optimum, solution (in the order of wholes and parts) and status.

`optimum` |
the value of the objective function at the optimum |

`solution` |
the vector of optimal coefficients (0-1vector) |

`status` |
an integer with status information about the solution returned. If the control parameter canonicalize_status is set (the default) then it will return 0 for the optimal solution being found, and non-zero otherwise. If the control parameter is set to FALSE it will return the GLPK status codes. |

Zhishi Wang, Michael Newton and Subhrangshu Nandi.

Zhishi W., Qiuling H., Bret L. and Michael N.: A multi-functional analyzer uses parameter constaints to improve the efficiency of model-based gene-set analysis (2013).

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