.\" -*- nroff -*- generated from .Rd format
.BG
.FN corGaus
.TL
Gaussian Correlation Structure
.DN
This function is a constructor for the `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 be later initialized using the appropriate
`initialize' method.
.CS
corGaus(value, form, nugget, metric, fixed)
.RA
.AG value
an optional vector with the parameter values in
constrained form. If `nugget' is `FALSE', `value' can
have only one element, corresponding to the "range" of the
Gaussian correlation structure, which must be greater than
zero. If `nugget' is `TRUE', meaning that a nugget effect
is present, `value' can contain one or two elements, the first
being the "range" and the second the "nugget effect" (one minus the
correlation between two observations taken arbitrarily close
together); the first must be greater than zero and the second must be
between zero and one. Defaults to `numeric(0)', which results in
a range of 90% of the minimum distance and a nugget effect of 0.1
being assigned to the parameters when `object' is initialized.
.AG form
a one sided formula of the form `~ S1+...+Sp', or
`~ S1+...+Sp | g', specifying spatial covariates `S1'
through `Sp' and,  optionally, a grouping factor `g'. 
When a grouping factor is present in `form', the correlation
structure is assumed to apply only to observations within the same
grouping level; observations with different grouping levels are
assumed to be uncorrelated. Defaults to `~ 1', which corresponds
to using the order of the observations in the data as a covariate,
and no groups.
.AG nugget
an optional logical value indicating whether a nugget
effect is present. Defaults to `FALSE'.
.AG metric
an optional character string specifying the distance
metric to be used. The currently available options are
`"euclidean"' for the root sum-of-squares of distances;
`"maximum"' for the maximum difference; and `"manhattan"'
for the sum of the absolute differences. Partial matching of
arguments is used, so only the first three characters need to be
provided.Defaults to `"euclidean"'.
.AG fixed
an optional logical value indicating whether the
coefficients should be allowed to vary in the optimization, or kept
fixed at their initial value. Defaults to `FALSE', in which case
the coefficients are allowed to vary.
.RT
an object of class `corGaus', also inheriting from class
`corSpatial', representing a Gaussian spatial correlation
structure.
.SH REFERENCES
Cressie, N.A.C. (1993), "Statistics for Spatial Data", J. Wiley & Sons.

Venables, W.N. and Ripley, B.D. (1997) "Modern Applied Statistics with
S-plus", 2nd Edition, Springer-Verlag.

Littel, Milliken, Stroup, and Wolfinger (1996) "SAS Systems for Mixed
Models", SAS Institute.
.SA
`initialize.corStruct', `dist'
.EX
sp1 <- corGaus(form = ~ x + y + z)
.KW models
.WR
