Description Usage Arguments Details Value Author(s) References See Also

Estimate the number of significant eigenvectors from the residuals of a high-dimensional data set given the model fit.

1 |

`dat` |
A m response variables by n samples matrix of data |

`mod` |
A n by k model matrix corresponding to the primary model fit, if mod is NULL, we estimate the number of significant eigenvectors in the expression data. (see model.matrix) |

`B` |
The number of null iterations to perform. |

`eigen.sig` |
The significance cutoff for eigenvectors. |

`seed` |
A numeric seed for reproducible results. Optional. |

Note that this function is a modified function from the package
`sva`

by Leek JT and Storey JD.
The model matrix should include a column for an intercept. sva.id
estimates the number of surrogate variables to include in the analysis
as described in Leek and Storey (2007).

A list containing:

`n.sv` |
The number of significant surrogate variables |

`p.sv` |
The p-values for each eigenvector |

Jeff T. Leek [email protected] and John D. Storey [email protected]

Leek JT and Storey JD (2007) Capturing heterogeneity in gene expression
studies by "surrogate variable analysis." *PLoS Genetics*,
**3**:e161.

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