corLin
class,
representing a linear spatial correlation structure. Letting
d denote the range and n denote the nugget
effect, the correlation between two observations a distance
r < d apart is 1-(r/d) when no nugget effect
is present and (1-n)*(1-(r/d)) when a nugget
effect is assumed. If r >= d the correlation is
zero. Objects created using this constructor must be later
initialized using the appropriate
initialize
method.
corLin(value, form, nugget, metric, fixed)
nugget
is
FALSE
,
value
can
have only one element, corresponding to the "range" of the
linear 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.
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.
FALSE
.
"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"
.
FALSE
, in which case
the coefficients are allowed to vary.
corLin
, also inheriting from class
corSpatial
, representing a linear spatial correlation
structure.
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.
sp1 <- corLin(form = ~ x + y)