GBIGM.test {GeneGeneInteR} | R Documentation

## GBIGM (Gene-based information gain method) for GGI analysis.

### Description

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

### Usage

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

### Arguments

`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.

### Details

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).

### Value

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.

### References

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.

### See Also

`GGI`

### Examples

    data(gene.pair)
    GBIGM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=500)

[Package GeneGeneInteR version 1.6.0 Index]