pCorShrinkData: Adaptive shrinkage of partial correlations from a data matrix
In CorShrink: Adaptive Shrinkage of Correlation and Covariance Matrices

Description Usage Arguments Value References

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 `lm` or `glmnet` indicating the type of regression used for the ISEE algorithm. If the number of samples is less than the number of features, the `reg_type` automatically reverts to `glmnet`.
`glmnet_alpha`	The alpha parameter ( a value between 0 and 1) as in the argument `alpha` in `glmnet` or `cv.glmnet` function of the glmnet package. When `glmnet_alpha=1`, it assumes a Lasso (L1) penalty on the regression and for `glmnet_alpha=0`, it assumes the ridge (L2) penalty. Only applicable when `reg_type="glmnet"`. Defaults to 1.
`glmnet_nfolds`	The number of folds of cross validation carried out in the `cv.glmnet` function of the glmnet package as part of the regression model estimation when `reg_type="glmnet"`. Defaults to 5.
`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.

CorShrink documentation built on July 13, 2018, 1 a.m.