lines.variomodel {geoR} | R Documentation |
This function adds a line with a variogram model specifyed by the user to a current variogram plot. The variogram is specifyed either by passing a list with values for the variogram elements or using each argument in the function.
lines.variomodel(x, ...) ## Default S3 method: lines.variomodel(x, cov.model, cov.pars, nugget, kappa, max.dist, scaled = FALSE, ...)
x |
a list with the values for the following components: cov.model , cov.pars ,
nugget , kappa (not aways compulsory, see argument
kappa below), max.dist .
This argument is optional if the other arguments in the function
are provided, otherwise is compulsory.
|
cov.model |
a string with the type of the variogram function. See
documentation of cov.spatial for further details.
|
cov.pars |
a vector or matrix with the values for the sill (sigmasq) and range (phi) parameters. |
nugget |
a scalar with the value of the nugget (tausq) parameter. |
kappa |
a scalar with the value of the smoothness
(kappa) parameters. Only required if cov.model
is one of the following: "matern" , "powered.exponential" ,
"cauchy" and "gneiting.matern"
|
max.dist |
maximum distance (x-axis) to compute and draw the line
representing the variogram model.
The default is the distance given by obj$max.dist . |
scaled |
logical. If TRUE the total sill in the plot is
equals to 1. |
... |
arguments to be passed to the function
curve . |
Adds a line with a variogram model to a plot.
In conjuction with plot.variogram
can be
used for instance to compare sample variograms against fitted models returned by
variofit
and/or likfit
.
A line with a variogram model is added to a plot on the current graphics device. No values are returned.
Paulo Justiniano Ribeiro Jr. Paulo.Ribeiro@est.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
Further information on the package geoR can be found at:
http://www.est.ufpr.br/geoR.
lines.variomodel.krige.bayes
,
lines.variomodel.grf
,
lines.variomodel.variofit
,
lines.variomodel.likGRF
,
plot.variogram
, lines.variogram
,
variofit
, likfit
, curve
.
data(s100) # compute and plot empirical variogram vario <- variog(s100, max.dist = 1) plot(vario) # estimate parameters vario.wls <- variofit(vario, ini = c(1, .3), fix.nugget = TRUE) # adds fitted model to the plot lines(vario.wls) # # Ploting different variogram models plot(0:1, 0:1, type="n") lines.variomodel(cov.model = "exp", cov.pars = c(.7, .25), nug = 0.3, max.dist = 1) # an alternative way to do this is: my.model <- list(cov.model = "exp", cov.pars = c(.7, .25), nugget = 0.3, max.dist = 1) lines.variomodel(my.model, lwd = 2) # now adding another model lines.variomodel(cov.m = "mat", cov.p = c(.7, .25), nug = 0.3, max.dist = 1, kappa = 1, lty = 2)