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.