| 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 YG.
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")
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.