pdNatural
class,
representing a general positive-definite matrix, using a natural
parametrization . If the matrix associated with object
is of
dimension $n$, it is represented by $n(n+1)/2$
parameters. Letting $\sigma_{ij}$ denote the $ij$-th
element of the underlying positive definite matrix and
$\rho_{ij}=\sigma_{i}/\sqrt{\sigma_{ii}\sigma_{jj}},\;i\neq j$ denote the associated
"correlations", the "natural" parameters are given by
$\sqrt{\sigma_{ii}}, \;i=1,\ldots,n$ and
$\log((1+\rho_{ij})/(1-\rho_{ij})),\; i \neq
j$. Note that all
natural parameters are individually unrestricted, but not jointly
unrestricted (meaning that not all unrestricted vectors would give
positive-definite matrices). Therefore, this parametrization should
NOT be used for optimization. It is mostly used for deriving
approximate confidence intervals on parameters following the
optimization of an objective function. When value
is
numeric(0)
, an uninitialized pdMat
object, a one-sided
formula, or a vector of character strings, object
is returned
as an uninitialized pdSymm
object (with just some of its
attributes and its class defined) and needs to have its coefficients
assigned later, generally using the coef
or matrix
replacement
functions. If value
is an initialized pdMat
object,
object
will be constructed from
as.matrix(value)
. Finally, if value
is a numeric
vector, it is assumed to represent the natural parameters of the
underlying positive-definite matrix.pdNatural(value, form, nam, data)
pdMat
object, a positive-definite
matrix, a one-sided linear formula (with variables separated by
+
), a vector of character strings, or a numeric
object
. Because
factors may be present in form
, the formula needs to be
evaluated on a data.frame to resolve the names itvalue
and form
. It is used to
obtain the levels for factors
, which affect the
dimensions and the row/column names of the underlying matrix. pdNatural
object representing a general positive-definite
matrix in natural parametrization, also inheriting from class
pdMat
.as.matrix.pdMat
, coef.pdMat
,
matrix<-.pdMat