CorShrinkVector: Adaptive shrinkage of a vector of correlations
In CorShrink: Adaptive Shrinkage of Correlation and Covariance Matrices

Description Usage Arguments Value References Examples

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).

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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 `corvec`. If provided, it is used as default. When set to NULL, asymptotic distribution of the Fisher z-scores is used using `nsamp_vec`.
`thresh_up`	Upper threshold for correlations in `corvec`. Defaults to 0.99
`thresh_down`	Lower threshold for correlations in `corvec`. Defaults to -0.99
`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

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 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)