Description Usage Arguments Details Value References Examples

Make a Direct Dependence Graph using the NCPC algorithm

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`obj` |
DDDataSet object |

`alpha` |
the alpha (P-value) cutoff for conditional independence tests |

`p.value.adjust.method` |
the multiple testing correction adjustment method |

`test.type` |
the type of conditional independence test (default: Monte Carlo x2 test "mc-x2-c" for binary data
and partial correlation "cor" for continuous data) . See the documentation
for |

`max.set.size` |
the maximal number of variables to condition on, if NULL estimated from number of positives in class labels (default: NULL) |

`mc.replicates` |
the number of Monte-carlo replicates, if applicable (default: 5000) |

`report.file` |
name of the file where a detailed report is to be printed, reporting is suppressed if NULL (default: NULL) |

`verbose` |
if to print out information about how the algorithm is progressing (default: TRUE) |

`star` |
if to use the NCPC* algorithm (default: FALSE) |

`min.table.size` |
the minimal number of samples in a contingency table per conditioning set (makes sense only for discrete data) |

Make a Direct Dependence Graph using a P-value and conditional independence tests. There are two version of the algorithm: NCPC and NCPC*. NCPC finds the causal neighbourhood while the NCPC* infers the full Markov Blanket.

The full algorithm is given in (Stojnic et al, 2012).

DDGraph object

R. Stojnic et al (2012): "A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles", in press, PloS Computational Biology.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
### load binary data for Mesoderm
data(mesoBin)
# run the NCPC algorithm with alpha=0.05 (on discrete data)
ncpc(mesoBin$Meso, alpha=0.05, test.type="mc-x2-c")
# run the NCPC* algorithm with alpha=0.05 (on discrete data)
res <- ncpc(mesoBin$Meso, alpha=0.05, test.type="mc-x2-c", star=TRUE)
# analysis of results:
class(res)
# although of class DDGraph, behaves much like a list
names(res)
# parameters used in obtaining results
res$params
# labels for each of the variables
res$final.calls
# direct variables
res$direct
### load continous data
data(mesoCont)
# run the NCPC algorith with alpha=0.05 (on continuous data)
ncpc(mesoCont$Meso, alpha=0.05, test.type="cor", max.set.size=1)
# run the NCPC* algorith with alpha=0.05 (on continuous data)
ncpc(mesoCont$Meso, alpha=0.05, test.type="cor", max.set.size=1, star=TRUE)
``` |

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