# PdofCSGt.bootstrap5: Parametric bootstrap for computing G-best and d-best PCS In PCS: Calculate the probability of correct selection (PCS)

## Description

Parametric bootstrap for computing G-best and d-best PCS. This function is called by the wrapper PCS.boot.par.

## Usage

 ```1 2``` ```PdofCSGt.bootstrap5(theta, T, D, G, B, SDE, dist = c("normal", "t"), df = 14, trunc = 6, est.names = c("O")) ```

## Arguments

 `theta` Vector of statistics (or parameters) from which it is desired to select the top t of them `T` Vector of the number of statistics (or parameters) desired to be selected `D` Vector of d-best selection parameters `G` Vector of G-best selection parameters `B` Bootstrap sample size `SDE` Standard error of the statistics theta (row-wise) `dist` Distributional assumption used for estimating PCS `df` Common degrees of freedom for one of the t-statistics in theta; the parameter is only used if dist="t" `trunc` Number of standard errors below the minimum selected population to disregard in the estimation of PCS; it is a truncation parameter to decrease run time `est.names` Kind of shrinkage estimator employed. Default estimator is "O" for the Olkin estimator. Other estimators will be considered for future releases.

## Value

An array, the non-empty part of which is a matrix whose rows are the entries of G or D and whose columns are the entries of T. If both G and D are entered, then a list is returned, where the \$G element is the G-best matrix, the \$d element is the d-best matrix.

## Author(s)

Jason Wilson, <jason.wilson@biola.edu>

## References

Cui, X. and Wilson, J. 2007. On How to Calculate the Probability of Correct Selection for Large k Populations. University of California, Riverside Statistics Department Technical Report 297. http://www.bubbs.biola.edu/~jason.wilson/Article2_tech_techreport.pdf

`PCS.boot.par`