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
y1 + ... + yp ~ x1 + ... + xq | block where the variables
on the left and right hand side may be measured on arbitrary scales
(including censored ones on the left hand side) and block is an
optional factor for stratification. |
data |
an optional data frame containing the variables in the
model formula. Alternatively, an object of class
exprSet may be specified. In this case,
all variables in formula , except . ,
are first evaluated in the pData data frame. The dot (. )
refers to the matrix of expression levels (exprs slot). |
subset |
an optional vector specifying a subset of observations to be used. |
weights |
an optional formula of the form ~ w defining
integer valued weights for the observations. |
object |
an object inheriting from class IndependenceProblem or an
object of class table . |
teststat |
a character, the type of test statistic to be applied: either a
standardized scalar test statistic (scalar ), or a
maximum type statistic (max ) or a quadratic form
(quad ). |
alternative |
a character, the alternative hypothesis must be
one of "two.sided" (default), "greater" or
"less" . You can specify just the initial letter. |
distribution |
a character, the null distribution of the test statistic
can be computed exact ly or can be approximated by its
asymptotic distribution (asymptotic )
or via Monte-Carlo resampling (approximate ). Alternatively, the functions
exact , approximate or asymptotic can be
used to specify how the exact conditional distribution of the test statistic
should be calculated or approximated. It is also possible to specify a
function with one argument (taking objects inheriting from
IndependenceTestStatistic )
which return an object of class NullDistribution . |
xtrafo |
a function of transformations (see trafo )
to be applied to the variables on the right hand side of
formula , see below. |
ytrafo |
a function of transformations (see trafo )
to be applied to the variables on the left hand side of
formula , see below. |
scores |
a named list of scores to be attached to ordered factors. In
case a variable is an unordered factor, it is coerced to
ordered first. |
check |
a function to be applied to objects of class
IndendenceTest in order to check for specific properties
of the data. |
... |
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)