PdofCSGt.bootstrap5 {PCS} | R Documentation |
Parametric bootstrap for computing G-best and d-best PCS. This function is called by the wrapper PCS.boot.par.
PdofCSGt.bootstrap5(theta, T, D, G, B, SDE, dist = c("normal", "t"), df = 14, trunc = 6, est.names = c("O"))
theta |
theta Vector of statistics (or parameters) from which it is desired to select
the top t of them |
T |
T Vector of the number of statistics (or parameters) desired to be selected |
D |
D Vector of d-best selection parameters |
G |
G Vector of G-best selection parameters |
B |
B Bootstrap sample size |
SDE |
SDE Standard error of the statistics theta (row-wise) |
dist |
dist Distributional assumption used for estimating PCS |
df |
df Common degrees of freedom for one of the t-statistics in theta;
the parameter is only used if dist="t" |
trunc |
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 |
est.names Kind of shrinkage estimator employed. Default estimator
is "O" for the Olkin estimator. Other estimators will be considered for future releases. |
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.
Jason Wilson, <jason.wilson@biola.edu>
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