G.H.Quad
(Package: PCS) :
Gauss-Hermite Quadrature function
Performs Gauss-Hermite Quadrature for an arbitrary number of nodes. Function for use in PofCSt.
● Data Source:
CranContrib
● Keywords: math
● Alias: G.H.Quad
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0 images
PofCSt
(Package: PCS) :
Probability of correct selection (PCS) for selecting t out of k populations
Implementation of the Gupta & Liang (1998) formula for computing the probability of correct selection (PCS) for selecting t out of k populations. The results are exact up to a user-settable tolerance parameter. This function is modular and is called by PdofCSt.T1or2, PdofCSt.cyc2, and PofCSGt.
PdofCSt.T1or2
(Package: PCS) :
Exact PCS, when selecting t=1 or t=2 or more populations
PdofCSt.T1or2 calls PofCSt to implement the d-best PCS formula for the case of t=1 or t=2 populations. It is called by PdofCSt.cyc2, which implements the d-best PCS formula for general t populations. PofCSGt calls PofCSt to implement the G-best formula for general t. These functions are modular and implement the time reduction techniques of Cui and Wilson (2007).
tindep
(Package: PCS) :
Standardized scores for two independent samples
Calculate the independent two-sample Welch t-statistics for k populations simultaneously. This function is used in PCS.bootstrap.np. It may also be used to summarize data from two sample experiments for use in PCS.exact and PCS.bootstrap.par.
PCS-package
(Package: PCS) :
Probability of Correct Selection (PCS)
These functions calculate the probability of correct selection (PCS) with G-best, d-best, and L-best selection as described in Cui & Wilson (2008) and Cui, Zhao, & Wilson (2008). The specific parameters (G,d,L), distributional assumptions (normal, Student's t, non-parametric), and calculation method (exact, bootstrap) are user-settable.
● Data Source:
CranContrib
● Keywords: htest, math
● Alias: PCS, PCS-package
●
0 images
PCS.boot.par
(Package: PCS) :
Probability of correct selection (PCS) calculator
These functions calculate the probability of correct selection (PCS) with G-best, d-best, and L-best selection as described in Cui & Wilson (2008) and Cui, Zhao, & Wilson (2008). The specific parameters (G,d,L), distributional assumptions (normal, Student's t, non-parametric), and calculation method (exact, bootstrap) are user-settable.
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