lines.variomodel
Adds a Line with a Variogram Model to a Variogram Plot
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.
Usage
# S3 method for variomodel
lines(x, …)
# S3 method for default
lines.variomodel(x, cov.model, cov.pars, nugget, kappa,
max.dist, scaled = FALSE, …)
Arguments
- x
a list with the values for the following components:
cov.model
,cov.pars
,nugget
,kappa
,max.dist
; or a numeric vector with values for x-axis values for the variogram (distances). This argument is not required if the other arguments in the function are provided, otherwise is compulsory. If a list is provided the arguments which match the list elements are ignored.- 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 partial sill (\(\sigma^2\)) and range (\(\phi\)) parameters.
- nugget
a scalar with the value of the nugget (\(\tau^2\)) 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. If a list is provided in
x
the default is the distance given byx$max.dist
. If a vector is provided inx
it takesmax(x)
.- scaled
logical. If
TRUE
the total sill in the plot is equals to \(1\).- …
arguments to be passed to the function
curve
.
Details
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
.
Value
A line with a variogram model is added to a plot on the current graphics device. No values are returned.
References
Further information on the package geoR can be found at: http://www.leg.ufpr.br/geoR.
See Also
lines.variomodel.krige.bayes
,
lines.variomodel.grf
,
lines.variomodel.variofit
,
lines.variomodel.likGRF
,
plot.variogram
, lines.variogram
,
variofit
, likfit
, curve
.
Examples
# NOT RUN {
# computing and ploting empirical variogram
vario <- variog(s100, max.dist = 1)
plot(vario)
# estimating parameters by weighted least squares
vario.wls <- variofit(vario, ini = c(1, .3), fix.nugget = TRUE)
# adding 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)
# adding the so-called "nested" models
# two exponential structures
lines.variomodel(seq(0,1,l=101), cov.model="exp",
cov.pars=rbind(c(0.6,0.15),c(0.4,0.25)), nug=0, col=2)
## exponential and spherical structures
lines.variomodel(seq(0,1,l=101), cov.model=c("exp", "sph"),
cov.pars=rbind(c(0.6,0.15), c(0.4,0.75)), nug=0, col=3)
# }