ggraph-tools | R Documentation |
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)
u |
|
copula |
copula object used for the Rosenblatt transform
( |
... |
additional arguments passed to the internal function
which computes the conditional copulas (for For |
cu.u |
|
N |
argument of |
iTest |
the result of (a version of) |
verbose |
|
idT.verbose |
logical, passed as |
piTest |
|
pvalues |
|
method |
|
globalFun |
|
(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.
If u
are distributed according to or “perfectly” sampled
from a copula, p-values on GOF tests for that copula should be uniformly
distributed in [0,1]
.
Hofert and Mächler (2014),
see pairsRosenblatt
.
pairsRosenblatt
for where these tools are used, including
demo(gof_graph)
for examples.
## demo(gof_graph)
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