CCA.test {GeneGeneInteR} | R Documentation

## CCA (Canonical-Correlation Analysis) based GGI analysis.

### Description

`CCA.test` performs a Gene-Gene Interaction (GGI) analysis based on the difference of canonical correlation between cases and controls.

### Usage

    CCA.test(Y, G1, G2, n.boot = 500)

### 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.boot` | positive integer. `n.boot` is the number of bootstrap replicates for estimating variances. By default, this is fixed to 500.

### Details

The test statistic is based on the difference between Fisher's transformation of the maximum of the canonical correlations in cases and controls. To calculate the test statistic for the interaction pvalue, `CCA.test` estimates the variance of the Fisher's transformation of the maximum of the canonical correlations in cases and controls using a bootstrap method.

### Value

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

`statistic` | The value of the statistic CCU.
`p.value` | The p-value for the test.
`estimate` | A vector of the Fisher's transformed maximum canonical correlation coefficient in cases and controls.
`parameter` | The number of boostrap samples used to estimate the p-value.
`null.value` | The value of CCU under the null hypothesis.
`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

Qianqian Peng, Jinghua Zhao, and Fuzhong Xue. A gene-based method for detecting gene-gene co-association in a case-control study. European Journal of Human Genetics, 18(5) :582-587, May 2010.

### See Also

`GGI`, `KCCA.test`

### Examples

    data(gene.pair)
    CCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)

[Package GeneGeneInteR version 1.20.0 Index]