Tools for computing a graphical goodness-of-fit (GOF) test based on pairwise Rosenblatt transformed data.
pairwiseCcop() computes a \((n,d,d)\)-array
which contains pairwise Rosenblatt-transformed data.
pairwiseIndepTest() takes such an array as input and
computes a \((d,d)\)-matrix of test results from
pairwise tests of independence (as by indepTest()).
pviTest() can be used to extract the matrix of p-values from
the return matrix of pairwiseIndepTest().
gpviTest() takes such a matrix of p-values and computes a global p-value with the method provided.
pairwiseCcop(u, copula, ...)
pairwiseIndepTest(cu.u, N=256,
iTest = indepTestSim(n, p=2, m=2, N=N, verbose = idT.verbose, ...),
verbose=TRUE, idT.verbose = verbose, ...) pviTest(piTest)
gpviTest(pvalues, method=p.adjust.methods, globalFun=min)
\((n,d,d)\)-array cu.u
with cu.u[i,j] containing \(C(u_i\,|\,u_j)\)
for \(i\neq j\) and \(u_i\) for \(i=j\).
\((d,d)\)-matrix of lists
with test results as returned by indepTest(). The
test results correspond to pairwise tests of independence as
conducted by indepTest().
\((d,d)\)-matrix of p-values.
global p-values for the specified methods.
\((n,d)\)-matrix of copula data.
copula object used for the Rosenblatt transform (\(H_0\) copula).
additional arguments passed to the internal function
which computes the conditional copulas (for pairwiseCcop()).
Can be used to pass, for example, the degrees of freedom
parameter df for t-copulas.
For pairwiseIndepTest(),
... are passed to indepTestSim().
\((n,d,d)\)-array as returned by
pairwiseCcop().
argument of indepTestSim().
the result of (a version of) indepTestSim();
as it does not depend on the data, and is costly to compute,
it can be computed separately and passed here.
integer (or logical)
indicating if and how much progress should be printed during the
computation of the tests for independence.
logical, passed as verbose argument to
indepTestSim().
\((d,d)\)-matrix of indepTest
objects as returned by pairwiseIndepTest().
\((d,d)\)-matrix of p-values.
character vector of adjustment methods for
p-values; see p.adjust.methods for more details.
function determining how to compute a
global p-value from a matrix of pairwise adjusted p-values.
Hofert and Mächler (2014),
see pairsRosenblatt.
pairsRosenblatt
for where these tools are used, including
demo(gof_graph) for examples.