ncpc: Make a Direct Dependence Graph using the NCPC algorithm...
In ddgraph: Distinguish direct and indirect interactions with Graphical Modelling

Description Usage Arguments Details Value References Examples

Make a Direct Dependence Graph using the NCPC algorithm

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ncpc(obj, alpha=0.05, p.value.adjust.method="none", test.type=c("mc-x2-c",
    "cor"), max.set.size=NULL, mc.replicates=5000, report.file=NULL,
    verbose=FALSE, star=FALSE, min.table.size=10)

`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 `ciTest` for other available conditional independence tests
`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.

### 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)