Transformations {coin} | R Documentation |
Rank-transformations for numerical data or dummy codings of factors.
trafo(data, numeric_trafo = id_trafo, factor_trafo = f_trafo, ordered_trafo = of_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 = 1 - minprob) logrank_trafo(x, ties.method = c("logrank", "HL", "average-scores")) f_trafo(x) of_trafo(x)
data |
an object of class |
numeric_trafo |
a function to by applied to |
factor_trafo |
a function to by applied to |
ordered_trafo |
a function to by applied to |
surv_trafo |
a function to by 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 those levels are interpreted as blocks.
|
x |
an object of classes |
ties.method |
two methods are available to adjust scores for ties.
Either the score generating function is applied to |
minprob |
a fraction between 0 and 0.5. |
maxprob |
a fraction between 0.5 and 1. |
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.
A named matrix with nrow(data)
rows and
arbitrary number of columns. User-supplied transformations must
return a numeric vector or matrix.
### dummy matrices, 2-sample problem (only one column) f_trafo(y <- gl(2, 5)) ### score matrices of_trafo(y <- ordered(gl(3, 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))