| RococoTestResults-class | R Documentation |
S4 class for storing results of the robust rank correlation test
Objects of this class can be created by calling rococo.test.
The following slots are defined for RococoTestResults objects:
count:number of times in which the test
statistic for a random shuffle exceeded the test statistic of the
true data; see rococo.test.
tnorm:list identifying t-norm to use or
two-argument function; see rococo.
If one of the standard choices "min", "prod", or
"lukasiewicz" has been used, the list has one component,
name that contains the string identifying the t-norm.
If a user-defined function has been used, the list has two
components: name contains "user-defined t-norm"
or the name attribute of the function object if available
and def contains the function object itself.
input:character string describing the input for which
rococo.test has been called.
length:number of samples for which
rococo.test has been called.
p.value:p-value of test.
p.value.approx:p-value as based on a normal approximation of the null distribution.
r.values:vector containing tolerance levels for the
two inputs; see rococo.test or
rococo.
numtests:number of (random) shuffles performed by
rococo.test.
exact:logical indicating whether p-value has been
computed exactly; see rococo.test.
similarity:character (vector) identifying the
similarity measure(s) used by rococo.test.
sample.gamma:test statistic (robust gamma rank
correlation coefficient) determined by
rococo.test.
H0gamma.mu:empirical mean of test statistic for random shuffles
H0gamma.sd:empirical standard deviation of test statistic for random shuffles
perm.gamma:in case rococo.test was
called with storeValues=TRUE, this slot contains the
vector of test statistics for random shuffles.
alternative:alternative hypothesis used by
rococo.test.
signature(object = "RococoTestResults"): d
displays the most important information stored in
object
Martin Krone and Ulrich Bodenhofer
https://github.com/UBod/rococo
U. Bodenhofer, M. Krone, and F. Klawonn (2013). Testing noisy numerical data for monotonic association. Inform. Sci. 245:21-37. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ins.2012.11.026")}.
U. Bodenhofer and F. Klawonn (2008). Robust rank correlation coefficients on the basis of fuzzy orderings: initial steps. Mathware Soft Comput. 15(1):5-20.
rococo.test, rococo,
show-methods
## create data
f <- function(x) ifelse(x > 0.9, x - 0.9, ifelse(x < -0.9, x + 0.9, 0))
x <- rnorm(25)
y <- f(x) + rnorm(25, sd=0.1)
## perform correlation tests
ret <- rococo.test(x, y, similarity="classical", alternative="greater")
show(ret)
ret <- rococo.test(x, y, similarity="linear", alternative="greater")
show(ret)
ret <- rococo.test(x, y, similarity=c("classical", "gauss"),
r=c(0, 0.1), alternative="greater",
numtests=10000)
show(ret)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.