IndependenceTest {coin} | R Documentation |
The independence between two sets of variables of arbitrary measurement scales, possibly stratified in blocks, is tested conditional on the data.
## S3 method for class 'formula' independence_test(formula, data, subset = NULL, weights = NULL, ...) ## S3 method for class 'IndependenceProblem' independence_test(object, teststat = c("max", "quad", "scalar"), distribution = c("asymptotic", "approximate", "exact"), alternative = c("two.sided", "less", "greater"), xtrafo = trafo, ytrafo = trafo, scores = NULL, check = NULL, ...) ## S3 method for class 'table' independence_test(object, distribution = c("asymptotic", "approximate"), ...)
formula |
a formula of the form
|
data |
an optional data frame containing the variables in the
model formula. Alternatively, an object of class
|
subset |
an optional vector specifying a subset of observations to be used. |
weights |
an optional formula of the form |
object |
an object inheriting from class |
teststat |
a character, the type of test statistic to be applied: either a
standardized scalar test statistic ( |
alternative |
a character, the alternative hypothesis must be
one of |
distribution |
a character, the null distribution of the test statistic
can be computed |
xtrafo |
a function of transformations (see |
ytrafo |
a function of transformations (see |
scores |
a named list of scores to be attached to ordered factors. In
case a variable is an unordered factor, it is coerced to
|
check |
a function to be applied to objects of class
|
... |
further arguments to be passed to or from methods. Currently, none of the additional arguments is passed to any function. |
The null hypothesis of the independence between the variables on the
left hand side and the variables on the
right hand side of formula
, possibly stratified by block
, is
tested. The vector supplied via the weights
argument is
interpreted as observation counts.
This function is the basic workhorse called by all other convenience
functions, mainly by supplying transformations via the xtrafo
argument and influence functions via the ytrafo
argument.
The scores
argument leads to linear-by-linear association tests
against ordered alternatives. If the formula y ~ x
was supplied and
both y
and x
are factors,
scores = list(y = 1:k, x = c(1, 4, 6))
first triggers a coercion
to class ordered
of both variables and attaches the list elements
as scores. The length of a score vector needs to be equal the number of
levels of the factor of interest.
The basis of this function is the framework for conditional inference procedures by Strasser & Weber (1999). The theory and this implementation are explained and illustrated in Hothorn, Hornik, van de Wiel and Zeileis (2006).
An object inheriting from class IndependenceTest-class
with
methods show
, statistic
, expectation
,
covariance
and pvalue
. The null distribution
can be inspected by pperm
, dperm
,
qperm
and support
methods.
Helmut Strasser \& Christian Weber (1999). On the asymptotic theory of permutation statistics. Mathematical Methods of Statistics 8, 220–250.
Torsten Hothorn, Kurt Hornik, Mark A. van de Wiel \& Achim Zeileis (2006). A Lego System for Conditional Inference. The American Statistician, 60(3), 257–263.
Torsten Hothorn, Kurt Hornik, Mark A. van de Wiel \& Achim Zeileis (2008). Implementing a class of permutation tests: The coin package, Journal of Statistical Software, 28(8), 1–23. http://www.jstatsoft.org/v28/i08/
### independence of asat and group via normal scores test independence_test(asat ~ group, data = asat, ### exact null distribution distribution = "exact", ### one-sided test alternative = "greater", ### apply normal scores to asat$asat ytrafo = function(data) trafo(data, numeric_trafo = normal_trafo), ### indicator matrix of 1st level of group xtrafo = function(data) trafo(data, factor_trafo = function(x) matrix(x == levels(x)[1], ncol = 1)) ) ### same as normal_test(asat ~ group, data = asat, distribution = "exact", alternative = "greater") ### if you are interested in the internals: ## Not run: browseURL(system.file("documentation", "html", "index.html", package = "coin")) ## End(Not run)