This set of functions is a modification of the
nlme. The modification is to replace the log parameterization used in
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.
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.
The following functions are provided:
mgcv:::coef.pdIdnot to access.)
Note that while the
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.
pdIdnot object, or related quantities. See the
nlme documentation for further details.
Simon N. Wood [email protected]
Pinheiro J.C. and Bates, D.M. (2000) Mixed effects Models in S and S-PLUS. Springer
nlme source code.
# see gamm
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