
likfit
.proflik(obj.likfit, geodata, coords = geodata$coords,
data = geodata$data, sill.values, range.values,
nugget.values, nugget.rel.values, lambda.values,
sillrange.values = TRUE, sillnugget.values = TRUE,
rangenugget.values = TRUE, sillnugget.rel.values = FALSE,
rangenugget.rel.values = FALSE, silllambda.values = FALSE,
rangelambda.values = TRUE, nuggetlambda.values = FALSE,
nugget.rellambda.values = FALSE,
uni.only = TRUE, bi.only = FALSE, ...)
likfit
, typically an
output of the function likfit
.coords
and
data
described next. Typically an object of the class
"geodata"
- a geoR data-set. If not provided the arguments
coords
and data
mustcoords
of the argument geodata
.data
of the argument geodata
.likfit
with the option fix.nugget = FA
likfit
with the option
likfit
with the optiouni.only = FALSE
."proflik"
which is
a list. Each element contains values of a parameter (or a pair of
parameters for 2-D profiles) and the
corresponding value of the profile likelihood.
The components of the output will vary according to the
input options.proflik.*
are auxiliary functions used to
compute the profile likelihoods. These functions are
internally called by the
minimization functions when estimating the model parameters.plot.proflik
for graphical output,
likfit
for the parameter estimation,
optim
and nlm
for further details about
the minimization functions.op <- par(no.readonly=TRUE)
if(is.R()) data(s100)
ml <- likfit(s100, ini=c(.5, .5), fix.nug=TRUE)
# a first atempt to find reasonable values for the x-axis:
prof <- proflik(ml, s100, sill.values=seq(0.5, 1.5, l=4),
range.val=seq(0.1, .5, l=4))
par(mfrow=c(1,2))
plot(prof)
# a nicer setting and now including 2-D profiles:
prof <- proflik(ml, s100, sill.values=seq(0.45, 2, l=11),
range.val=seq(0.1, .55, l=11), uni.only=F)
par(mfrow=c(2,2))
plot(prof, nlevels=16)
par(op)
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