| pdLogChol {nlme} | R Documentation |
General Positive-Definite Matrix
Description
This function is a constructor for the pdLogChol class,
representing a general positive-definite matrix. If the matrix
associated with object is of dimension n, it is
represented by n(n+1)/2 unrestricted parameters,
using the log-Cholesky parametrization described in Pinheiro and
Bates (1996).
When
valueisnumeric(0), an uninitializedpdMatobject, a one-sided formula, or a character vector,objectis returned as an uninitializedpdLogCholobject (with just some of its attributes and its class defined) and needs to have its coefficients assigned later, generally using thecoeformatrixreplacement functions.If
valueis an initializedpdMatobject,objectwill be constructed fromas.matrix(value).Finally, if
valueis a numeric vector, it is assumed to represent the unrestricted coefficients of the matrix-logarithm parametrization of the underlying positive-definite matrix.
Usage
pdLogChol(value, form, nam, data)
Arguments
value |
an optional initialization value, which can be any of the
following: a |
form |
an optional one-sided linear formula specifying the
row/column names for the matrix represented by |
nam |
an optional character vector specifying the row/column names
for the matrix represented by object. It must have length equal to
the dimension of the underlying positive-definite matrix and
unreplicated elements. This argument is ignored when
|
data |
an optional data frame in which to evaluate the variables
named in |
Details
Internally, the pdLogChol representation of a symmetric
positive definite matrix is a vector starting with the logarithms of
the diagonal of the Choleski factorization of that matrix followed by
its upper triangular portion.
Value
a pdLogChol object representing a general positive-definite
matrix, also inheriting from class pdMat.
Author(s)
José Pinheiro and Douglas Bates bates@stat.wisc.edu
References
Pinheiro, J.C. and Bates., D.M. (1996) Unconstrained Parametrizations for Variance-Covariance Matrices, Statistics and Computing 6, 289–296.
Pinheiro, J.C., and Bates, D.M. (2000) Mixed-Effects Models in S and S-PLUS, Springer.
See Also
as.matrix.pdMat,
coef.pdMat,
pdClasses,
matrix<-.pdMat
Examples
(pd1 <- pdLogChol(diag(1:3), nam = c("A","B","C")))
(pd4 <- pdLogChol(1:6))
(pd4c <- chol(pd4)) # -> upper-tri matrix with off-diagonals 4 5 6
pd4c[upper.tri(pd4c)]
log(diag(pd4c)) # 1 2 3