# PdCSGt.bootstrap.NP2: Non-Parametric Bootstrap for Computing G-best and d-best PCS In PCS: Calculate the Probability of Correct Selection (PCS)

 PdCSGt.bootstrap.NP2 R Documentation

## Non-Parametric Bootstrap for Computing G-best and d-best PCS

### Description

Non-parametric bootstrap for computing G-best and d-best PCS. This function is called by the wrapper PCS.boot.np.

### Usage

```PdCSGt.bootstrap.NP2(X1, X2, T, D, G, N, trunc = 6)
```

### Arguments

 `X1` k by n1 matrix of data. k is the number of populations and n1 the sample size of the first treatment. `X2` k by n2 matrix of data. k is the number of populations and n2 the sample size of the second treatment. `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 `N` The bootstrap sample size `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

### Value

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. 2009. A Simulation Study on the Probability of Correct Selection for Large k Populations. Communications in Statistics: Simulation and Computation. 38:6. https://docs.google.com/a/biola.edu/viewer?a=v&pid=sites&srcid=YmlvbGEuZWR1fGphc29ud2lsc29ufGd4OjMxYTdjNjJkNzY3NTU4NzA

`PCS.boot.np`