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corGaus class,
representing a Gaussian spatial correlation structure. Letting
$d$ denote the range and $n$ denote the nugget
effect, the correlation between two observations a distance
$r$ apart is $\exp(-(r/d)^2)$ when no nugget
effect is present and $(1-n) \exp(-(r/d)^2)$
when a nugget effect is assumed. Objects created using this
constructor must later be initialized using the appropriate
` Initialize method.corGaus(value, form, nugget, metric, fixed)nugget is FALSE, value can
have only one element, corresponding to the "range" of the
Gaussian correlation structure, which must be greate~ S1+...+Sp, or
~ S1+...+Sp | g, specifying spatial covariates S1
through Sp and, optionally, a grouping factor g.
When a grouping factor is presenFALSE."euclidean" for the root sum-of-squares of distances;
"maximum" for the maximum difference; and "manhattanFALSE, in which case
the coefficients are allowed to vary.corGaus, also inheriting from class
corSpatial, representing a Gaussian spatial correlation
structure.Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.
Initialize.corStruct,
summary.corStruct,
distsp1 <- corGaus(form = ~ x + y + z)
# example lme(..., corGaus ...)
# Pinheiro and Bates, pp. 222-249
fm1BW.lme <- lme(weight ~ Time * Diet, BodyWeight,
random = ~ Time)
# p. 223
fm2BW.lme <- update(fm1BW.lme, weights = varPower())
# p 246
fm3BW.lme <- update(fm2BW.lme,
correlation = corExp(form = ~ Time))
# p. 249
fm8BW.lme <- update(fm3BW.lme, correlation = corGaus(form = ~ Time))Run the code above in your browser using DataLab