variog.model.env | R Documentation |
Computes envelopes for a empirical variogram by simulating data for given model parameters.
Computes bootstrap paremeter estimates
variog.model.env(geodata, coords = geodata$coords, obj.variog,
model.pars, nsim = 99, save.sim = FALSE, messages)
boot.variofit(geodata, coords = geodata$coords, obj.variog,
model.pars, nsim = 99, trace = FALSE, messages)
geodata |
a list containing element |
coords |
an |
obj.variog |
an object of the class |
model.pars |
a list with model specification and parameter
values. The input is typically an object of the class
|
nsim |
number of simulations used to compute the envelopes. Defaults to 99. |
save.sim |
logical. Indicates whether or not the simulated data
are included in the output. Defaults to |
trace |
logical. If |
messages |
logical. If |
The envelopes are computed assuming a (transformed) Gaussian random field model. Simulated values are generated at the data locations, given the model parameters. The empirical variogram is computed for each simulation using the same lags as for the original variogram of the data. The envelopes are computed by taking, at each lag, the maximum and minimum values of the variograms for the simulated data.
An object of the class
"variogram.envelope"
which is a
list with the components:
u |
a vector with distances. |
v.lower |
a vector with the minimum variogram values at each
distance in |
v.upper |
a vector with the maximum variogram values at each
distance in |
simulations |
a matrix with the simulated data.
Only returned if |
Paulo Justiniano Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR/.
variog.mc.env
for envelops computed by
using data permutation,
variog
for variogram calculations,
plot.variogram
and variog.mc.env
for
graphical output. The functions
likfit
, variofit
are used to estimate the model parameters.
s100.ml <- likfit(s100, ini = c(0.5, 0.5), fix.nugget = TRUE)
s100.vario <- variog(s100, max.dist = 1)
s100.env <- variog.model.env(s100, obj.v = s100.vario,
model.pars = s100.ml)
plot(s100.vario, env = s100.env)
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