Description Usage Arguments Value References Examples
View source: R/CorShrinkMatrix.R
This function performs adaptive shrinkage of a matrix of pairwise 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 4 |
cormat |
A matrix of pairwise correlations - not necessarily a correlation matrix. NAs in this matrix are treated as 0. |
nsamp |
An integer or a matrix denoting the number of samples for
each pair of variables over which the correlation has been computed.
Only used when |
zscore_sd |
A matrix of the sandard error of the Fisher z-scores for each pair of
variables. May contain NA-s as well. The NA-s in this matrix must
match with the NAs in the |
thresh_up |
Upper threshold for correlations in |
thresh_down |
Lower threshold for correlations in |
image |
character. options for plotting the original or the corshrink matrix.
If |
tol |
The tolerance chosen to check how far apart the CorShrink matrix is from the nearest positive definite matrix before applying PD completion. |
image.control |
Control parameters for the image when
|
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 a list with adaptively shrunk version
of the sample correlation matrix both before (ash_cor_only
) and after
PD completion (ash_cor_PD
). 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 | data("pairwise_corr_matrix")
data("common_samples")
out <- CorShrinkMatrix(pairwise_corr_matrix, common_samples)
|
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