
Builds the trend matrix in accordance to a specification of the mean provided by the user.
trend.spatial(trend, geodata, add.to.trend)
An object of the class trend.spatial
which is an
trend
matrix, where
is the number of spatial
locations and
specifies the mean part of the model.
See DETAILS
below.
optional. An object of the class geodata
as described in
as.geodata
.
optional. Specifies aditional terms to the mean part of the model. See details below.
Paulo J. Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
The implicity model assumes that there is an underlying process
with mean trend
defines the form of the mean and the
following options are allowed:
"cte"
the mean is assumed to be constant over the region,
in which case
"1st"
the mean is assumed to be a first order polynomial
on the coordinates:
"2nd"
the mean is assumed to be a second order polynomial
on the coordinates:
~ model
a model specification. See
formula
for further details on how to specify
a model in R using formulas. Notice that the model term before
the ~
is not necessary.
Typically used to include covariates
(external trend) in the model.
Denote by
trend = "1st"
and trend = ~ x1 + x2
trend = "2nd"
and trend = ~ x1 + x2 + I(x1^2) + I(x2^2) + I(x1*x2)
Search path for covariates
Typically, functions in the package geoR which calls
trend.spatial
will have the arguments geodata
,
coords
and data
.
When the trend is specifed as trend = ~ model
the terms included in the model will be searched for in the following
path sequence (modified in version 1.7-6, no longer attach objects):
in the object geodata
(coerced to data-frame)
in the users/session Global environment
in the session search path
The argument add.to.trend
adds terms to what is specified in
the argument trend
. This seems redundant but allow
specifications of the type: trend="2nd", add.trend=~other.covariates
.
Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR/.
The section DETAILS
in the documentation for
likfit
for more about the underlying model.
# a first order polynomial trend
trend.spatial("1st", sic.100)[1:5,]
# a second order polynomial trend
trend.spatial("2nd", sic.100)[1:5,]
# a trend with a covariate
trend.spatial(~altitude, sic.100)[1:5,]
# a first degree trend plus a covariate
trend.spatial(~coords+altitude, sic.100)[1:5,]
# with produces the same as
trend.spatial("1st", sic.100, add=~altitude)[1:5,]
# and yet another exemple
trend.spatial("2nd", sic.100, add=~altitude)[1:5,]
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