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

An internal function to smooth a set of sample covariance matrices by computing empirical Bayes posterior means.

1 | ```
squeezeMVar(S, df, Lambda = NULL, nu = NULL)
``` |

`S` |
a list of covariance matrices |

`df` |
numeric vector of degrees of freedom for covariance matrices |

`Lambda` |
use this target covariance matrix instead of calculating it from the data |

`nu` |
use this nu instead of calculating it from the data |

Calculate shrinkage estimates for covariance matrices using the procedure of Tai and Speed (2006) and Smyth (2004)

`varPost ` |
list of posterior covariance matrices |

`varPrior ` |
target covariance matrix |

`dfPrior ` |
prior degrees of freedom |

Martin Aryee

Smyth, G. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology (2004) vol. 3

Tai, Y and Speed, T. A multivariate empirical Bayes statistic for replicated microarray time course data. Annals of Statistics (2006) vol. 34 (5) pp. 2387-2412

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