| ggraph-tools {copula} | R Documentation |
Computations for Graphical GOF Test via Pairwise Rosenblatt Transforms
Description
Tools for computing a graphical goodness-of-fit (GOF) test based on pairwise Rosenblatt transformed data.
pairwiseCcop()computes a
(n,d,d)-arraywhich contains pairwise Rosenblatt-transformed data.pairwiseIndepTest()takes such an array as input and computes a
(d,d)-matrixof test results from pairwise tests of independence (as byindepTest()).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.
Usage
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)
Arguments
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 |
|
Value
- pairwiseCcop
(n,d,d)-arraycu.uwithcu.u[i,j]containingC(u_i\,|\,u_j)fori\neq jandu_ifori=j.- pairwiseIndepTest
(d,d)-matrixof lists with test results as returned byindepTest(). The test results correspond to pairwise tests of independence as conducted byindepTest().- pviTest
(d,d)-matrixof p-values.- gpviTest
global p-values for the specified methods.
Note
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].
References
Hofert and Mächler (2014),
see pairsRosenblatt.
See Also
pairsRosenblatt
for where these tools are used, including
demo(gof_graph) for examples.
Examples
## demo(gof_graph)