| maxstat.test {maxstat} | R Documentation |
Performs a maximally selected rank or Gauss test on bivariate observations.
maxstat.test(x, y, event = NULL, smethod=c("Gauss", "Wilcoxon", "Median",
"NormalQuantil","LogRank"), pmethod=c("none", "Lau92",
"Lau94", "exactGauss", "HL", "min"),
minprop = 0.1, maxprop=0.9, alpha = NULL, ...)
maxstat.test(formula, data, subset, na.action, ...)
x |
numeric vector of data values, independent variable. |
y |
numeric vector of data values, dependent variable. |
event |
censoring: 0=alive, 1=dead (only meaningful with LogRank) |
smethod |
kind of statistic to be computed. |
pmethod |
kind of p-value approximation to be used. |
minprop |
at least minprop*100% of the observations in the
first group. |
maxprop |
not more than minprop*100% of the observations in
the first group. |
alpha |
significance niveau, the appropriate quantile is computed if
alpha is specified. Used for plotting within plot.maxtest. |
formula |
a formula describing the model to be tested of the form
y ~ x where y is the dependent and x the independent
variable. For survival problems, i.e. using the log-rank statistic, the
formula is of the form Surv(time, event) ~ x, see above. |
data |
an optional 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 NAs. Defaults to
getOption("na.action"). |
... |
additional parameters to methods. |
The assessment of the predictive power of a variable x for a
dependent variable y can be determined by a maximally selected rank
or Gauss test.
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 log-rank
scores. Gauss means a maximally selected Gauss statistic.
pmethod specifies which kind of approximation of the p-value 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 p-value for a maximally selected Gauss
statistic, see Hothorn and Lausen (2002).
HL is a small sample approximation based on the Streitberg-R"ohmel
algorithm (see pperm) introduced by Hothorn and
Lausen (2002). This requires integer
valued scores. For v. d. Waerden and Log-rank 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 (2002).
All the approximations are known to be conservative, so min gives
the minimum p-value of all procedures.
For survival problems, i.e. using a maximally selected log-rank statistic,
the interface is similar to survfit. The depended
variable is a survival object Surv(time, event) (note: in contrast to
Surv in package survival only time and event
can be specified). The argument
event may be a numeric vector of 0 (alive) and 1
(dead) or a vector of logicals with TRUE indicating death.
A list of class maxtest containing the following components
is returned:
statistic |
the value of the test statistic. |
p.value |
the p-value for the test. |
method |
the type of test and p-value approximation applied. |
estimate |
the estimated cutpoint (of x) which separates
y best. |
plot.maxtest and print.maxtest can be used for
plotting and printing.
Hothorn, T. and Lausen, B. (2002). On the Exact Distribution of Maximally Selected Rank Statistics. submitted
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, Physica-Verlag, 483–496
Hothorn, T. (2001). On Exact Rank Tests in R. R News, 1, 11–12
x <- sort(runif(20))
y <- c(rnorm(10), rnorm(10, 2))
mod <- maxstat.test(y ~ x, smethod="Wilcoxon", pmethod="HL",
minprop=0.25, maxprop=0.75, alpha=0.05)
print(mod)
plot(mod)