| 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 - valueis- numeric(0), an uninitialized- pdMatobject, a one-sided formula, or a character vector,- objectis returned as an uninitialized- pdLogCholobject (with just some of its attributes and its class defined) and needs to have its coefficients assigned later, generally using the- coefor- matrixreplacement functions.
- If - valueis an initialized- pdMatobject,- objectwill be constructed from- as.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