Transformations: Functions for Data Transformation

Description

Transformations for factors and numeric variables.

Usage

id_trafo(x)
rank_trafo(x, ties.method = c("mid-ranks", "random"))
normal_trafo(x, ties.method = c("mid-ranks", "average-scores"))
median_trafo(x, mid.score = c("0", "0.5", "1"))
savage_trafo(x, ties.method = c("mid-ranks", "average-scores"))
consal_trafo(x, ties.method = c("mid-ranks", "average-scores"), a = 5)
koziol_trafo(x, ties.method = c("mid-ranks", "average-scores"), j = 1)
klotz_trafo(x, ties.method = c("mid-ranks", "average-scores"))
mood_trafo(x, ties.method = c("mid-ranks", "average-scores"))
ansari_trafo(x, ties.method = c("mid-ranks", "average-scores"))
fligner_trafo(x, ties.method = c("mid-ranks", "average-scores"))
logrank_trafo(x, ties.method = c("mid-ranks", "Hothorn-Lausen",
                                 "average-scores"),
              weight = logrank_weight, ...)
logrank_weight(time, n.risk, n.event,
               type = c("logrank", "Gehan-Breslow", "Tarone-Ware", "Prentice",
                        "Prentice-Marek", "Andersen-Borgan-Gill-Keiding",
                        "Fleming-Harrington", "Self"),
               rho = NULL, gamma = NULL)
f_trafo(x)
of_trafo(x, scores = NULL)
maxstat_trafo(x, minprob = 0.1, maxprob = 1 - minprob)
fmaxstat_trafo(x, minprob = 0.1, maxprob = 1 - minprob)
ofmaxstat_trafo(x, minprob = 0.1, maxprob = 1 - minprob)
trafo(data, numeric_trafo = id_trafo, factor_trafo = f_trafo,
      ordered_trafo = of_trafo, surv_trafo = logrank_trafo,
      var_trafo = NULL, block = NULL)
mcp_trafo(...)

Arguments

an object of class "numeric", "factor", "ordered" or "Surv".

ties.method

a character, the method used to handle ties. The score generating function either uses the mid-ranks ("mid-ranks", default) or, in the case of rank_trafo, randomly broken ties ("random"). Alternatively,

mid.score

a character, the score assigned to observations exactly equal to the median: either 0 ("0", default), 0.5 ("0.5") or 1 ("1"); see median_test.

a numeric vector, the values taken as the constant $a$ in the Conover-Salsburg scores. Defaults to 5.

a numeric, the value taken as the constant $j$ in the Koziol-Nemec scores. Defaults to 1.

weight

a function where the first three arguments must correspond to time, n.risk, and n.event given below. Defaults to logrank_weight.

time

a numeric vector, the ordered distinct time points.

n.risk

a numeric vector, the number of subjects at risk at each time point specified in time.

n.event

a numeric vector, the number of events at each time point specified in time.

type

a character, one of "logrank" (default), "Gehan-Breslow", "Tarone-Ware", "Prentice", "Prentice-Marek", "Andersen-Borgan-Gill-Keiding", "Fleming-Harrington"

rho

a numeric vector, the $\rho$ constant when type is "Tarone-Ware", "Fleming-Harrington" or "Self"; see logrank_test. Defaults to NULL

gamma

a numeric vector, the $\gamma$ constant when type is
    "Fleming-Harrington" or "Self"; see
    logrank_test.  Defaults to NULL, implying 0

scores

a numeric vector or list, the scores corresponding to each level of an
    ordered factor.  Defaults to NULL, implying 1:nlevels(x).

minprob

a numeric, a fraction between 0 and 0.5; see maxstat_test.
    Defaults to 0.1.

maxprob

a numeric, a fraction between 0.5 and 1; see maxstat_test.
    Defaults to 1 - minprob.

data

an object of class "data.frame".

numeric_trafo

a function to be applied to elements of class "numeric" in
    data, returning a matrix with nrow(data) rows and an arbitrary
    number of columns.  Defaults to id_trafo.

factor_trafo

a function to be applied to elements of class "factor" in
    data, returning a matrix with nrow(data) rows and an arbitrary
    number of columns.  Defaults to f_trafo.

ordered_trafo

a function to be applied to elements of class "ordered" in
    data, returning a matrix with nrow(data) rows and an arbitrary
    number of columns.  Defaults to of_trafo.

surv_trafo

a function to be applied to elements of class "Surv" in data,
    returning a matrix with nrow(data) rows and an arbitrary number of
    columns.  Defaults to logrank_trafo.

var_trafo

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

block

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

...

logrank_trafo: further arguments to be passed to weight.    mcp_trafo: factor name and contrast matrix (as matrix or character)
    in a tag = value format for multiple comparisons based on a single

`Value`

A numeric vector or matrix with nrow(x) rows and an arbitrary number of
  columns.  For trafo, a named matrix with nrow(data) rows and an
  arbitrary number of columns.

`Details`

The utility functions documented here are used to define specialized test
  procedures.
  id_trafo is the identity transformation.
  rank_trafo, normal_trafo, median_trafo,
  savage_trafo, consal_trafo and koziol_trafo compute rank
  scores, normal scores, median scores, Savage scores, Conover-Salsburg scores
  (see neuropathy) and Koziol-Nemec scores, respectively, for
  location problems.
  klotz_trafo, mood_trafo, ansari_trafo and
  fligner_trafo compute Klotz scores, Mood scores, Ansari-Bradley scores
  and Fligner-Killeen scores, respectively, for scale problems.
  logrank_trafo computes weighted logrank scores for censored data,
  allowing for a user-defined weight function through the weight argument
  (see GTSG).
  f_trafo computes dummy matrices for factors and of_trafo assigns
  scores to ordered factors.
  maxstat_trafo, fmaxstat_trafo and ofmaxstat_trafo compute
  scores for cutpoint problems (see maxstat_test).
  trafo applies its arguments to the elements of data according to
  the classes of the elements.  A trafo function with modified default
  arguments is usually supplied to independence_test via the
  xtrafo or ytrafo arguments.  Fine tuning, i.e., different
  transformations for different variables, is possible by supplying a named list
  of functions to the var_trafo argument.
  mcp_trafo computes contrast matrices for factors.

`Examples`

Run this code## Dummy matrix, two-sample problem (only one column)
f_trafo(gl(2, 3))

## Dummy matrix, K-sample problem (K columns)
x <- gl(3, 2)
f_trafo(x)

## Score matrix
ox <- as.ordered(x)
of_trafo(ox)
of_trafo(ox, scores = c(1, 3:4))
of_trafo(ox, scores = list(s1 = 1:3, s2 = c(1, 3:4)))

## Normal scores
y <- runif(6)
normal_trafo(y)

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

## The same, but allows for fine-tuning
trafo(data.frame(x = x, ox = ox, y = y), var_trafo = list(y = normal_trafo))

## Transformations for maximally selected statistics
maxstat_trafo(y)
fmaxstat_trafo(x)
ofmaxstat_trafo(ox)

## Apply transformation blockwise (as in the Friedman test)
trafo(data.frame(y = 1:20), numeric_trafo = rank_trafo, block = gl(4, 5))

## Multiple comparisons
dta <- data.frame(x)
mcp_trafo(x = "Tukey")(dta)

## The same, but useful when specific contrasts are desired
K <- rbind("2 - 1" = c(-1,  1, 0),
           "3 - 1" = c(-1,  0, 1),
           "3 - 2" = c( 0, -1, 1))
mcp_trafo(x = K)(dta)
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