This set of functions is a modification of the pdMat class pdIdent
from library nlme. The modification is to replace the log parameterization used in pdMat
with a notLog2 parameterization, since the latter avoids
indefiniteness in the likelihood and associated convergence problems: the
parameters also relate to variances rather than standard deviations, for
consistency with the pdTens class. The functions are particularly useful for
working with Generalized Additive Mixed Models where variance parameters/smoothing parameters can
be very large or very small, so that overflow or underflow can be a problem.
These functions would not normally be called directly, although unlike the
pdTens class it is easy to do so.
pdIdnot(value = numeric(0), form = NULL,
nam = NULL, data = sys.frame(sys.parent()))A class pdIdnot object, or related quantities. See the nlme documentation for further details.
Initialization values for parameters. Not normally used.
A one sided formula specifying the random effects structure.
a names argument, not normally used with this class.
data frame in which to evaluate formula.
Simon N. Wood simon.wood@r-project.org
The following functions are provided: Dim.pdIndot, coef.pdIdnot, corMatrix.pdIdnot,
logDet.pdIdnot, pdConstruct.pdIdnot, pdFactor.pdIdnot, pdMatrix.pdIdnot,
solve.pdIdnot, summary.pdIdnot. (e.g. mgcv:::coef.pdIdnot to access.)
Note that while the pdFactor and pdMatrix functions return the inverse of the scaled random
effect covariance matrix or its factor, the pdConstruct function is initialised with estimates of the
scaled covariance matrix itself.
Pinheiro J.C. and Bates, D.M. (2000) Mixed effects Models in S and S-PLUS. Springer
The nlme source code.
te, pdTens, notLog2, gamm