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)
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