thresholdSelection: Threshold selection for equality of correlation matrices test
In ldstatsHD: Linear Dependence Statistics for High-Dimensional Data

Description Usage Arguments Details Value Author(s) References Examples

This function finds the threshold that maximizes an estimate of the power of the exceedances-based test.

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thesholdSelection(D1, D2, useq, deltaA = 3, deltaB = 10, nite = 500, 
                  excAdj = FALSE, alpha = 0.05, paired = TRUE)

`D1`	first population dataset in matrix n\times p form.
`D2`	second population dataset in matrix n\times p form.
`useq`	sequence of threshold levels to be used.
`deltaA`	shape hyperparameter for gamma prior distribution of Fisher transform correlation differences in absolute value.
`deltaB`	scale hyperparameter for gamma prior distribution of Fisher transform correlation differences in absolute value.
`nite`	number of generated samples.
`excAdj`	weight for the exceedances test. If `excAdj = FALSE` the test statistic is given by the squared exceedances. In contrast, If `excAdj = TRUE` the test statistic is given by the squared of the exceedances minus the threshold.
`alpha`	null hypothesis rejection level.
`paired`	if `TRUE`, observations in `D1` and `D2` are assumed to be matched (n_1 must be equal to n_2).
`...`	arguments passed to or from other methods to the low level.

Details are given in references.

Optimal threshold

Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina, Claus Mayer and Ioannis Papastathopoulos.

To come

#### data
EX2 <- pcorSimulatorJoint(nobs = 50, nclusters = 3, nnodesxcluster = c(40,40,40), 
                          pattern = "pow", diffType = "cluster", dataDepend = "diag", 
                          pdiff=0.5)			               
						 
#### best threshold
useq <- seq(0,qnorm(1-0.01/2),length.out=150)
thesholdSelection(EX2$D1,EX2$D2, useq)