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"org M"uller & 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.
Torsten Hothorn & Achim Zeileis (2008). Generalized maximally selected statistics, Biometrics, 64(4), 1263–1269.
### 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")))