PdofCSGt.bootstrap5: Parametric Bootstrap for Computing G-best and d-best PCS

In PCS: Calculate the Probability of Correct Selection (PCS)

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

Description

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

Usage

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 <[e-mail]>

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. https://docs.google.com/a/biola.edu/viewer?a=v&pid=sites&srcid=YmlvbGEuZWR1fGphc29ud2lsc29ufGd4OjJmYTY2YTJjY2EwYjg2ZmY

See Also

PCS.boot.par

PCS documentation built on June 21, 2022, 1:05 a.m.