This is a service routine for
the estimated covariance matrix of the data from an
lme object, allowing the
user control about which levels of random effects to include in this
extract.lme.cov forms the full matrix explicitly:
extract.lme.cov2 tries to be more economical than this.
A fitted model object returned by a call to
The data frame/ model frame that was supplied to
The level of nesting at which to start including random effects in the calculation. This is used to allow smooth terms to be estimated as random effects, but treated like fixed effects for variance calculations.
The random effects, correlation structure and variance structure used for a linear mixed model combine to imply a covariance matrix for the response data being modelled. These routines extracts that covariance matrix. The process is slightly complicated, because different components of the fitted model object are stored in different orders (see function code for details!).
extract.lme.cov calculation is not optimally efficient, since it forms the full matrix,
which may in fact be sparse.
extract.lme.cov2 is more efficient. If the
covariance matrix is diagonal, then only the leading diagonal is returned; if
it can be written as a block diagonal matrix (under some permutation of the
original data) then a list of matrices defining the non-zero blocks is
returned along with an index indicating which row of the original data each
row/column of the block diagonal matrix relates to. The block sizes are defined by
the coarsest level of grouping in the random effect structure.
extract.lme.cov does not currently deal with the situation in which the
grouping factors for a correlation structure are finer than those for the
extract.lme.cov2 does deal with this situation.
extract.lme.cov an estimated covariance matrix.
extract.lme.cov2 a list containing the estimated covariance matrix
and an indexing array. The covariance matrix is stored as the elements on the
leading diagonal, a list of the matrices defining a block diagonal matrix, or
a full matrix if the previous two options are not possible.
Simon N. Wood email@example.com
Pinheiro J.C. and Bates, D.M. (2000) Mixed effects Models in S and S-PLUS. Springer
For details of how GAMMs are set up here for estimation using
Wood, S.N. (2006) Low rank scale invariant tensor product smooths for Generalized Additive Mixed Models. Biometrics 62(4):1025-1036
Wood S.N. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC Press.
## see also ?formXtViX for use of extract.lme.cov2 require(mgcv) library(nlme) data(Rail) b <- lme(travel~1,Rail,~1|Rail) extract.lme.cov(b) extract.lme.cov2(b)
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