exactRankTests (version 0.8-6)

cscores: Computation of Scores

Description

This function can be used to compute several scores for a data vector.

Usage

## S3 method for class 'default':
cscores(y, type=c("Data", "Wilcoxon", "NormalQuantile", 
        "AnsariBradley", "Median", "Savage", "ConSal"), int=FALSE,
        maxs=length(y), ...)
## S3 method for class 'factor':
cscores(y, \dots)
## S3 method for class 'Surv':
cscores(y, type="LogRank", int=FALSE, maxs=nrow(y), ...)

Arguments

y

a numeric, factor or logical vector or an object of class Surv.

type

a character string which specifies the type of the scores to be computed. Data just returns y if y is numeric.

int

a logical, forcing integer valued scores.

maxs

an integer defining the maximal value of the scores if int=TRUE.

...

additional arguments, not passed to anything at the moment.

Value

• A vector of scores for y with an attribute scores indicating the kind of scores used is returned.

Details

This function will serve as the basis for a more general framework of rank and permutation tests in future versions of this package. Currently, it is only used in the examples.

The logrank scores are computed as given in Hothorn & Lausen (2002).

If integer valued scores are requested (int = TRUE), the scores are mapped into integers by round(scores*length(scores)/max(scores)). See dperm for more details.

type is self descriptive, except for ConSal which implements scores suggested by Conover & Salsburg (1988).

References

Torsten Hothorn & Berthold Lausen (2002), On the exact distribution of maximally selected rank statistics. Computational Statistics & Data Analysis, (in press). Preprint available from http://www.mathpreprints.com/math/Preprint/hothorn/20020227/2

William J. Conover & David S. Salsburg (1988), Locally most powerful tests for detecting treatment effects when only a subset of patients can be expected to "respond" to treatment. Biometrics, 44, 189-196.

Examples

y <- rnorm(50)
# v.d. Waerden scores
nq <- cscores(y, type="Normal", int=TRUE)
# quantile for m=20 observations in the first group
qperm(0.1, nq, 20)