Functions for Data Transformation
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(...)x |
an object of class |
ties.method |
a character, the method used to handle ties. The score generating function
either uses the mid-ranks ( |
mid.score |
a character, the score assigned to observations exactly equal to the median:
either 0 ( |
a |
a numeric vector, the values taken as the constant a in the
Conover-Salsburg scores. Defaults to |
j |
a numeric, the value taken as the constant j in the Koziol-Nemec
scores. Defaults to |
weight |
a function where the first three arguments must correspond to |
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 |
n.event |
a numeric vector, the number of events at each time point specified in
|
type |
a character, one of |
rho |
a numeric vector, the ρ constant when |
gamma |
a numeric vector, the γ constant when |
scores |
a numeric vector or list, the scores corresponding to each level of an
ordered factor. Defaults to |
increment |
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
|
minprob |
a numeric, a fraction between 0 and 0.5; see |
maxprob |
a numeric, a fraction between 0.5 and 1; see |
data |
an object of class |
numeric_trafo |
a function to be applied to elements of class |
factor_trafo |
a function to be applied to elements of class |
ordered_trafo |
a function to be applied to elements of class |
surv_trafo |
a function to be applied to elements of class |
var_trafo |
an optional named list of functions to be applied to the corresponding
variables in |
block |
an optional factor whose levels are interpreted as blocks. |
... |
|
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 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.
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.
Starting with coin version 1.1-0, all transformation functions are now
passing through missing values (i.e., NAs). Furthermore,
median_trafo and logrank_trafo are now increasing
functions (in conformity with most other transformations in this package).
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.