View source: R/estimationCKT.datasetPairs.R
datasetPairs | R Documentation |
In (Derumigny, & Fermanian (2019)), it is described how the problem
of estimating conditional Kendall's tau can be rewritten as a
classification task for a dataset of pairs (of observations).
This function computes such a dataset, that can be then used to
estimate conditional Kendall's tau using one of the following
functions:
CKT.fit.tree
, CKT.fit.randomForest
,
CKT.fit.GLM
, CKT.fit.nNets
,
CKT.predict.kNN
.
datasetPairs(
X1,
X2,
Z,
h,
cut = 0.9,
onlyConsecutivePairs = FALSE,
nPairs = NULL
)
X1 |
vector of observations of the first conditioned variable. |
X2 |
vector of observations of the second conditioned variable. |
Z |
vector or matrix of observations of the conditioning variable(s),
of dimension |
h |
the bandwidth. Can be a vector; in this case,
the components of |
cut |
the cutting level to keep a given pair or not.
Used only if no |
onlyConsecutivePairs |
if |
nPairs |
number of most relevant pairs to keep in the final datasets.
If this is different than the default |
A matrix with (4+dimZ)
columns and n*(n-1)/2
rows
if onlyConsecutivePairs=FALSE
and else (n/2)
rows.
It is structured in the following way:
column 1
contains the information
about the concordance of the pair (i,j) ;
columns 2
to 1+dimZ
contain
the mean value of Z (the conditioning variables) ;
column 2+dimZ
contains the value of the kernel K_h(Z_j - Z_i) ;
column 3+dimZ
and 4+dimZ
contain the corresponding values of i and j.
Derumigny, A., & Fermanian, J. D. (2019). A classification point-of-view about conditional Kendall’s tau. Computational Statistics & Data Analysis, 135, 70-94. (Algorithm 1 for all pairs and Algorithm 8 for the case of only consecutive pairs) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2019.01.013")}
the functions that require such a dataset of pairs
to do the estimation of conditional Kendall's tau:
CKT.fit.tree
, CKT.fit.randomForest
,
CKT.fit.GLM
, CKT.fit.nNets
,
CKT.predict.kNN
, and CKT.fit.randomForest
.
# We simulate from a conditional copula
N = 500
Z = rnorm(n = N, mean = 5, sd = 2)
conditionalTau = 0.9 * pnorm(Z, mean = 5, sd = 2)
simCopula = VineCopula::BiCopSim(N = N , family = 3,
par = VineCopula::BiCopTau2Par(1 , conditionalTau) )
X1 = qnorm(simCopula[,1])
X2 = qnorm(simCopula[,2])
datasetP = datasetPairs(
X1 = X1, X2 = X2, Z = Z, h = 0.07, cut = 0.9)
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