Description Usage Arguments Details Value References Examples
Performs a test of independence of a response and one or more covariables using maximally selected rank statistics.
1 2 3 4 5 6  ## S3 method for class 'data.frame'
maxstat.test(formula, data, subset, na.action, ...)
maxstat(y, x=NULL, weights = NULL, smethod=c("Wilcoxon", "Median",
"NormalQuantil","LogRank", "Data"), pmethod=c("none", "Lau92",
"Lau94", "exactGauss", "HL", "condMC", "min"), iscores=(pmethod=="HL"),
minprop = 0.1, maxprop=0.9, alpha = NULL, keepxy=TRUE, ...)

y 
numeric vector of data values, dependent variable. 
x 
numeric vector of data values, independent variable. 
weights 
an optional numeric vector of nonnegative weights, summing to the number of observations. 
smethod 
kind of statistic to be computed, i.e. defines the scores to be used for computing the statistic. 
pmethod 
kind of pvalue approximation to be used. 
iscores 
logical: should the scores be mapped into integers

minprop 
at least 
maxprop 
not more than 
alpha 
significance niveau, the appropriate quantile is computed if

keepxy 
logical: return 
formula 
a formula describing the model to be tested of the form

data 
an data frame containing the variables in the
model formula. 
subset 
an optional vector specifying a subset of observations to be used. 
na.action 
a function which indicates what should happen when
the data contain 
... 
additional parameters to be passed to

The assessment of the predictive power of a variable x
for a
dependent variable y
can be determined by a maximally selected rank
statistic.
smethod
determines the kind of statistic to be used.
Wilcoxon
and Median
denote maximally selected
Wilcoxon and Median statistics. NormalQuantile
and
LogRank
denote v.d. Waerden and logrank
scores.
pmethod
specifies which kind of approximation of the pvalue should
be used. Lau92
is the limiting distribution by a Brownian bridge
(see Lausen and Schumacher, 1992, and pLausen92
),
Lau94
the approximation based on an improved Bonferroni
inequality (see Lausen, Sauerbrei and Schumacher, 1994, and pLausen94
).
exactGauss
returns the exact pvalue for a maximally selected Gauss
statistic, see Hothorn and Lausen (2003).
HL
is a small sample approximation based on the StreitbergR\"ohmel
algorithm (see pperm
) introduced by Hothorn and
Lausen (2003). This requires integer
valued scores. For v. d. Waerden and Logrank scores we try to find
integer valued scores having the same shape. This results in slightly
different scores (and a different test), the procedure is described in
Hothorn (2001) and Hothorn and Lausen (2003).
All the approximations are known to be conservative, so min
gives
the minimum pvalue of all procedures.
condMC
simulates the distribution via conditional MonteCarlo.
For survival problems, i.e. using a maximally selected logrank statistic,
the interface is similar to survfit
. The depended
variable is a survival object Surv(time, event)
. The argument
event
may be a numeric vector of 0
(alive) and 1
(dead) or a vector of logicals with TRUE
indicating death.
If more than one covariable is specified in the right hand side of
formula
(or if x
is a matrix or data frame), the variable with
smallest pvalue is selected and the pvalue for the global test problem of
independence of y
and every variable on the right hand side is
returned (see Lausen et al., 2002).
An object of class maxtest
or mmaxtest
(if more than one
covariable was specified) containing the following components
is returned:
statistic 
the value of the test statistic. 
p.value 
the pvalue for the test. 
smethod 
the type of test applied. 
pmethod 
the type of pvalue approximation applied. 
estimate 
the estimated cutpoint (of 
maxstats 
a list of 
whichmin 
an integer specifying the element of 
p.value 
the pvalue of the global test. 
univp.values 
the pvalues for each of the variables under test. 
cm 
the correlation matrix the pvalue is based on. 
plot.maxtest
and print.maxtest
can be used for
plotting and printing. If keepxy = TRUE
, there are elements y
and x
giving the response and independent variable.
Hothorn, T. and Lausen, B. (2003). On the Exact Distribution of Maximally Selected Rank Statistics. Computational Statistics & Data Analysis, 43, 121–137.
Lausen, B. and Schumacher, M. (1992). Maximally Selected Rank Statistics. Biometrics, 48, 73–85
Lausen, B., Sauerbrei, W. and Schumacher, M. (1994). Classification and Regression Trees (CART) used for the exploration of prognostic factors measured on different scales. in: P. Dirschedl and R. Ostermann (Eds), Computational Statistics, Heidelberg, PhysicaVerlag, 483–496
Hothorn, T. (2001). On Exact Rank Tests in R. R News, 1, 11–12
Lausen, B., Hothorn, T., Bretz, F. and Schmacher, M. (2004). Assessment of Optimally Selected Prognostic Factors. Biometrical Journal, 46(3), 364–374.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  set.seed(29)
x < sort(runif(20))
y < c(rnorm(10), rnorm(10, 2))
mydata < data.frame(cbind(x,y))
mod < maxstat.test(y ~ x, data=mydata, smethod="Wilcoxon", pmethod="HL",
minprop=0.25, maxprop=0.75, alpha=0.05)
print(mod)
plot(mod)
# adjusted for more than one prognostic factor.
library("survival")
mstat < maxstat.test(Surv(time, cens) ~ IPI + MGE, data=DLBCL,
smethod="LogRank", pmethod="exactGauss",
abseps=0.01)
plot(mstat)
### sphase
set.seed(29)
data("sphase", package = "TH.data")
maxstat.test(Surv(RFS, event) ~ SPF, data=sphase, smethod="LogRank",
pmethod="Lau94")
maxstat.test(Surv(RFS, event) ~ SPF, data=sphase, smethod="LogRank",
pmethod="Lau94", iscores=TRUE)
maxstat.test(Surv(RFS, event) ~ SPF, data=sphase, smethod="LogRank",
pmethod="HL")
maxstat.test(Surv(RFS, event) ~ SPF, data=sphase, smethod="LogRank",
pmethod="condMC", B = 9999)
plot(maxstat.test(Surv(RFS, event) ~ SPF, data=sphase, smethod="LogRank"))

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