Bingham | R Documentation |
Score matching estimators for the Bingham distribution's parameter matrix. Two methods are available: a full score matching method that estimates the parameter matrix directly and a hybrid method by \insertCitemardia2016sc;textualscorematchingad that uses score matching to estimate just the eigenvalues of the parameter matrix.
Bingham(Y, A = NULL, w = rep(1, nrow(Y)), method = "Mardia")
Y |
A matrix of multivariate observations in Cartesian coordinates. Each row is a multivariate measurement (i.e. each row corresponds to an individual). |
A |
For full score matching only: if supplied, then NA elements of |
w |
An optional vector of weights for each measurement in |
method |
Either "Mardia" or "hybrid" for the hybrid score matching estimator from \insertCitemardia2016sc;textualscorematchingad or "smfull" for the full score matching estimator. |
The Bingham distribution has a density proportional to
\exp(z^T A z),
where A
is a symmetric matrix and the trace (sum of the diagonals) of A
is zero for identifiability \insertCite@p181, @mardia2000discorematchingad.
The full score matching method estimates all elements of A
directly except the final element of the diagonal, which is calculated from the sum of the other diagonal elements to ensure that the trace of A
is zero.
The method by \insertCitemardia2016sc;textualscorematchingad first calculates the maximum-likelihood estimate of the eigenvectors G
of A
.
The observations Y
are then standardised to Y
G
.
This standardisation corresponds to diagonalising A
where the eigenvalues of A
become the diagonal elements of the new A
.
The diagonal elements of the new A
are then estimated using score matching, with the final diagonal element calculated from the sum of the other elements.
See \insertCitemardia2016sc;textualscorematchingad for details.
A list of est
, SE
and info
.
est
contains the estimated matrix A
and a vector form, paramvec
, of A
(ordered according to c(diag(A)[1:(p-1)], A[upper.tri(A)])
). For the Mardia method, the estimated eigenvalues of A
(named evals
) and eigenvectors of A
(named G
) are also returned.
SE
contains estimates of the standard errors if computed. See cppad_closed()
.
info
contains a variety of information about the model fitting procedure and results.
Other directional model estimators:
FB()
,
vMF()
,
vMF_robust()
p <- 4
A <- rsymmetricmatrix(p)
A[p,p] <- -sum(diag(A)[1:(p-1)]) #to satisfy the trace = 0 constraint
if (requireNamespace("simdd")){
Y <- simdd::rBingham(100, A)
Bingham(Y, method = "Mardia")
}
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