Description Usage Arguments Details Value References See Also Examples

`PLSPM.test`

performs a Gene-Gene Interaction (GGI) analysis based on the
modelisation of a netwrok of statistical relations. The aim is to quantify the
connections between the latent and the manifest variables.

1 | ```
PLSPM.test(Y, G1, G2,n.perm=500)
``` |

`Y` |
numeric or factor vector with exactly two different values. |

`G1` |
SnpMatrix object.
Must have a number of rows equal to the length of |

`G2` |
SnpMatrix object.
Must have a number of rows equal to the length of |

`n.perm` |
positive integer. |

The PLSPM-based method, as described in Zhang et al. (2013), aims at comparing the path coefficients *β_D* and *β_C*, where *β_D* is calculated among cases and *β_C* among controls. The co-association between genes `G1`

and `G2`

is defined by:

*U=\frac{β_D-β_C}{√{Var(β_D-β_C)}}.*

The `plspm`

R package is used to estimate *U*. The significance pvalue is obtained by using a permutation method on the difference between the path coefficients.

A list with class `"htest"`

containing the following components:

`statistic` |
The value of the statistic U. |

`p.value` |
The p-value for the test. |

`estimate` |
A vector of the path coefficients in cases and controls. |

`parameter` |
The number of boostrap samples used to estimate the p-value. |

`null.value` |
The value of U 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. |

X. Zhang et al. (2013) A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design. PLoS ONE, 8(4) :e62129.

1 2 |

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