Description Usage Arguments Value References

View source: R/pCorShrinkData.R

Performs adaptive shrinkage of the sample inverse covariances and
consequently partial correlation matrix starting from a data matrix ( no NA
permitted unlike `CorShrinkData`

). The procedure of shrinkage combines
inverse covariance derivations from the ISEE algorithm of [Fan and Lv, 2016]
with the `CorShrink`

formulation.

1 2 3 | ```
pCorShrinkData(dat, permu_type = c("random", "ordered"), reg_type = "lm",
glmnet_alpha = NULL, glmnet_nfolds = 5, thresh_up = 0.99,
thresh_down = -0.99, maxiter = 1000, ash.control = list())
``` |

`dat` |
The samples by features data matrix. Does not permit NA entries in X. |

`permu_type` |
Determines if the columns of the data matrix (the features) are permuted randomly before blocking in the ISEE algorithm [see reference] or not.Takes one of two character input values - "random" and "ordered" - depending on if the columns are permuted or not. |

`reg_type` |
Either equals |

`glmnet_alpha` |
The alpha parameter ( a value between 0 and 1) as in the argument |

`glmnet_nfolds` |
The number of folds of cross validation carried out in
the |

`thresh_up` |
Upper threshold for correlations. Defaults to 0.99 |

`thresh_down` |
Lower threshold for correlations. Defaults to -0.99. |

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

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

Returns an adaptively shrunk version of the inverse covariance matrix and the partial correlation matrix estimate.

False Discovery Rates: A New Deal. Matthew Stephens bioRxiv 038216; doi: http://dx.doi.org/10.1101/038216 Fan, Y. and Lv, J., 2016. Innovated scalable efficient estimation in ultra-large Gaussian graphical models. The Annals of Statistics, 44(5), pp.2098-2126.

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