Transformations for factors and numeric variables.
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",
"Peto-Peto", "Prentice", "Prentice-Marek",
"Andersen-Borgan-Gill-Keiding", "Fleming-Harrington",
"Gaugler-Kim-Liao", "Self"),
rho = NULL, gamma = NULL)
f_trafo(x)
of_trafo(x, scores = NULL)
zheng_trafo(x, increment = 0.1)
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(...)
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.
an object of class "numeric"
, "factor"
, "ordered"
or
"Surv"
.
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,
the average of the scores resulting from applying the score generating
function to randomly broken ties are used ("average-scores"
). See
logrank_test()
for a detailed description of the methods used
in logrank_trafo()
.
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
.
a function where the first three arguments must correspond to time
,
n.risk
, and n.event
given below. Defaults to
logrank_weight
.
a numeric vector, the ordered distinct time points.
a numeric vector, the number of subjects at risk at each time point
specified in time
.
a numeric vector, the number of events at each time point specified in
time
.
a character, one of "logrank"
(default), "Gehan-Breslow"
,
"Tarone-Ware"
, "Peto-Peto"
, "Prentice"
,
"Prentice-Marek"
, "Andersen-Borgan-Gill-Keiding"
,
"Fleming-Harrington"
, "Gaugler-Kim-Liao"
or "Self"
; see
logrank_test()
.
a numeric vector, the \(\rho\) constant when type
is
"Tarone-Ware"
, "Fleming-Harrington"
, "Gaugler-Kim-Liao"
or "Self"
; see logrank_test()
. Defaults to
NULL
, implying 0.5
for type = "Tarone-Ware"
and
0
otherwise.
a numeric vector, the \(\gamma\) constant when type
is
"Fleming-Harrington"
, "Gaugler-Kim-Liao"
or "Self"
; see
logrank_test()
. Defaults to NULL
, implying 0
.
a numeric vector or list, the scores corresponding to each level of an
ordered factor. Defaults to NULL
, implying 1:nlevels(x)
.
a numeric, the score increment between the order-restricted sets of scores.
A fraction greater than 0, but smaller than or equal to 1. Defaults to
0.1
.
a numeric, a fraction between 0 and 0.5; see maxstat_test()
.
Defaults to 0.1
.
a numeric, a fraction between 0.5 and 1; see maxstat_test()
.
Defaults to 1 - minprob
.
an object of class "data.frame"
.
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
.
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
.
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
.
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
.
an optional named list of functions to be applied to the corresponding
variables in data
. Defaults to NULL
.
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
unordered factor; see mcp()
in package
multcomp.
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 (Wilcoxon) scores, normal (van der Waerden) scores, median (Mood-Brown)
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 right-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. For ordered factors with two levels, the
scores are normalized to the \([0, 1]\) range. zheng_trafo()
computes a finite collection of order-restricted scores for ordered factors
(see jobsatisfaction
, malformations
and
vision
).
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.
## 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)))
zheng_trafo(ox, increment = 1/3)
## 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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