# 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

1 | ```
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. http://www.bubbs.biola.edu/~jason.wilson/Article2_sim_revised02.pdf

### See Also

`PCS.boot.np`