coin (version 0.4-2)

Transformations: Functions for Data Transformations

Description

Rank-transformations for numerical data or dummy codings of factors.

Usage

trafo(data, numeric_trafo = id_trafo, 
    factor_trafo = f_trafo, surv_trafo = logrank_trafo, 
    var_trafo = NULL, block = NULL)
id_trafo(x)
ansari_trafo(x, ties.method = c("mid-ranks", "average-scores"))
fligner_trafo(x, ties.method = c("mid-ranks", "average-scores"))
normal_trafo(x, ties.method = c("mid-ranks", "average-scores"))
median_trafo(x)
consal_trafo(x, ties.method = c("mid-ranks", "average-scores"))
maxstat_trafo(x, minprob = 0.1, maxprob = 0.9)
logrank_trafo(x, ties.method = c("logrank", "HL"))
f_trafo(x)

Arguments

data

an object of class data.frame.

numeric_trafo

a function to by applied to numeric elements of data returning a matrix with nrow(data) rows and an arbitrary number of columns.

factor_trafo

a function to by applied to factor elements of data returning a matrix with nrow(data) rows and an arbitrary number of columns (usually a dummy or contrast matrix).

surv_trafo

a function to by applied to elements of class Surv of data returning a matrix with nrow(data) rows and an arbitrary number of columns.

var_trafo

an optional named list of functions to be applied to the corresponding variables in data.

block

an optional factor those levels are interpreted as blocks. trafo is applied to each level of block separately.

x

an object of classes numeric, factor or Surv.

ties.method

two methods are available to adjust scores for ties. Either the score generating function is applied to mid-ranks or scores, based on random ranks, are averaged average-scores. For ties handling in case of censored d

minprob

a fraction between 0 and 0.5.

maxprob

a fraction between 0.5 and 1.

Value

• A named matrix with nrow(data) rows and arbitrary number of columns.

Details

The utility functions documented here are used to define special independence tests.

trafo applies its arguments to the elements of data according to the classes of the elements.

id_trafo is the identity transformation and f_trafo computes dummy matrices for factors.

ansari_trafo and fligner_trafo compute Ansari-Bradley or Fligner scores for scale problems.

normal_trafo, median_trafo and consal_trafo implement normal scores, median scores or Conover-Salburg scores (see neuropathy) for location problems, logrank_trafo returns logrank scores for censored data.

A trafo function with modified default arguments is usually feeded into independence_test via the xtrafo or ytrafo arguments.

Fine tuning (different transformations for different variables) is possible by supplying a named list of functions to the var_trafo argument.

Examples

### dummy matrices, 2-sample problem (only one column)
f_trafo(y <- gl(2, 5))

### K-sample problem (K columns)
f_trafo(y <- gl(5, 2))

### normal scores
normal_trafo(x <- rnorm(10))

### and now together
trafo(data.frame(x = x, y = y), numeric_trafo = normal_trafo)

### the same, more flexible when multiple variables are in play
trafo(data.frame(x = x, y = y), var_trafo = list(x = normal_trafo))

### maximally selected statistics
maxstat_trafo(rnorm(10))

### apply transformation blockwise (e.g. for Friedman test)
trafo(data.frame(y = 1:20), numeric_trafo = rank, block = gl(4, 5))