GBIGM.test: GBIGM (Gene-based information gain method) for GGI analysis.
In GeneGeneInteR: Tools for Testing Gene-Gene Interaction at the Gene Level

Description Usage Arguments Details Value References See Also Examples

GBIGM.test performs a Gene-Gene Interaction (GGI) analysis by contrasting the information entropy between cases and controls.

1	GBIGM.test(Y, G1, G2, n.perm = 1000)

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`n.perm`	positive integer. `n.perm` is the number of permutations performed to compute the pvalue. By default, this is fixed to 1000.

First, the conditional entropy and information gain rate are computed for each gene G1 and G2. In a second step, information gain rate for the gene pair (G1,G2) is computed. A p-value is estimated using permutations of Y. More details can be found in Li et al. (2015).

A list with class "htest" containing the following components:

`statistic`	The value of the statistic DeltaR1,2.
`p.value`	The p-value for the test.
`estimate`	The estimation of DeltaR1,2.
`parameter`	The number of permutations used to estimate the p-value.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

J. Li, et al.. A gene-based information gain method for detecting gene-gene interactions in case-control studies. European Journal of Human Genetics, 23 :1566-1572, 2015.

GGI