Description Usage Arguments Value References Examples

View source: R/CorShrinkVector.R

This function performs adaptive shrinkage of a vector of sample correlations using a mixture normal prior on Fisher z-scores , with each component centered at the same base level z-score value (0 for 0 base correlation) but a wide range of data-driven component variances. The method is similar to the adaptive shrinkage method for modeling false discovery rates proposed in Stephens 2016 (see reference).

1 2 3 | ```
CorShrinkVector(corvec, nsamp_vec, zscore_sd_vec = NULL, thresh_up = 0.99,
thresh_down = -0.99, report_model = FALSE, maxiter = 1000,
ash.control = list())
``` |

`corvec` |
A vector of sample correlations (may contain NAs) |

`nsamp_vec` |
A vector of the number of samples over which correlations for each cell of the vector are estimated. |

`zscore_sd_vec` |
A vector of the sandard error of the Fisher z-scores for each cell
in the vector. May contain NA-s as well. The NA-s in this matrix must
match with the NAs in |

`thresh_up` |
Upper threshold for correlations in |

`thresh_down` |
Lower threshold for correlations in |

`report_model` |
if TRUE, outputs the full adaptive shrinkage output, else outputs the shrunken vector. Defaults to FALSE. |

`maxiter` |
The maximum number of iterations run for the adaptive shrinkage EM algorithm. Default is 1000. |

`ash.control` |
The control parameters for adaptive shrinkage |

If `report_model = FALSE`

, returns an adaptively shrunk version
of the vector of correlations. If `report_model = TRUE`

, then the
function also returns all the details of the adaptive shrinkage model output.

False Discovery Rates: A New Deal. Matthew Stephens bioRxiv 038216; doi: http://dx.doi.org/10.1101/038216

1 2 3 | ```
cor_vec <- c(-0.56, -0.4, 0.02, 0.2, 0.9, 0.8, 0.3, 0.1, 0.4)
nsamp_vec <- c(10, 20, 30, 4, 50, 60, 20, 10, 3)
out <- CorShrinkVector(corvec = cor_vec, nsamp_vec = nsamp_vec)
``` |

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