| MaxstatTest {coin} | R Documentation |
Testing the independence of a set of ordered or numeric covariates and a response of arbitrary measurement scale against cutpoint alternatives.
## S3 method for class 'formula':
maxstat_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem':
maxstat_test(object,
distribution = c("asymptotic", "approximate"),
teststat = c("maxtype", "quadtype"),
minprob = 0.1, maxprob = 0.9, ...)
formula |
a formula of the form y ~ x1 + ... + xp | block where y
is a variable measured at arbitrary scale and the covariates x1
to xp are at least of class ordered; block is an
optional factor for stratification. |
data |
an optional data frame containing the variables in the model formula. |
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. |
distribution |
a character, the null distribution of the test statistic
can be approximated by its asymptotic distribution (asymptotic)
or via Monte-Carlo resampling (approximate).
Alternatively, the functions
approximate or asymptotic can be
used to specify how the exact conditional distribution of the test statistic
should be calculated or approximated. |
teststat |
a character, the type of test statistic to be applied: a
maximum type statistic (maxtype) or a quadratic form
(quadform). |
minprob |
a fraction between 0 and 0.5;
consider only cutpoints greater than
the minprob * 100 % quantile of x. |
maxprob |
a fraction between 0.5 and 1;
consider only cutpoints smaller than
the maxprob * 100 % quantile of x. |
... |
further arguments to be passed to or from methods. |
The null hypothesis of independence of all covariates to the response
y against simple cutpoint alternatives is tested.
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.
Rupert Miller & David Siegmund (1982), Maximally Selected Chi Square Statistics. Biometrics 38, 1011–1016.
Berthold Lausen & Martin Schumacher (1992), Maximally Selected Rank Statistics. Biometrics 48, 73–85.
Torsten Hothorn & Berthold Lausen (2003), On the Exact Distribution of Maximally Selected Rank Statistics. Computational Statistics & Data Analysis 43, 121–137.
Berthold Lausen, Torsten Hothorn, Frank Bretz & Martin Schumacher (2004), Optimally Selected Prognostic Factors. Biometrical Journal 46, 364–374.
data("treepipit", package = "coin")
maxstat_test(counts ~ coverstorey, data = treepipit)