| 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("max", "quad"),
minprob = 0.1, maxprob = 1 - minprob, ...)
formula |
a formula of the form y ~ x1 + ... + xp | block where y
and covariates x1 to xp can be variables measured at arbitrary scales;
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 (max) or a quadratic form
(quad). |
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.
For an unordered covariate x, all possible partitions into two
groups are evaluated. The cutpoint is then a set of levels defining
one of the two groups.
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.
J{"o}rg M{"u}ller & Torsten Hothorn (2004). Maximally Selected Two-Sample Statistics as a new Tool for the Identification and Assessment of Habitat Factors with an Application to Breeding Bird Communities in Oak Forests. European Journal of Forest Research, 123, 218–228.
### analysis of the tree pipit data in Mueller and Hothorn (2004)
maxstat_test(counts ~ coverstorey, data = treepipit)
### and for all possible covariates (simultaneously)
mt <- maxstat_test(counts ~ ., data = treepipit)
show(mt)$estimate
### reproduce applications in Sections 7.2 and 7.3
### of Hothorn & Lausen (2003) with limiting distribution
maxstat_test(Surv(time, event) ~ EF, data = hohnloser,
ytrafo = function(data) trafo(data, surv_trafo = function(x)
logrank_trafo(x, ties = "HL")))
maxstat_test(Surv(RFS, event) ~ SPF, data = sphase,
ytrafo = function(data) trafo(data, surv_trafo = function(x)
logrank_trafo(x, ties = "HL")))