# we will look at some different variogram estimates
 plot(parana.cloud,col="red")
 lines.variomodel(fit)

#Use Cressie's robust variogram estimator
 parana.cressie <- variog(parana,uvec = max(parana.cloud$u)*seq(0,1,l=11)
	,estimator.type="modulus")
 fit.cressie <- variofit(parana.cressie,c(5000,250),"gaussian")
     
#Compare the fitted values
 summary(fit)
 summary(fit.cressie)

 lines.variomodel(fit.cressie,lty=2)

#What if we use a weighted least squares estimator?
 fit.wls <- variofit(parana.bin,c(5000,250),"gaussian",wei="cressie",nugget=275)
 lines.variomodel(fit.wls,lty=3)

#Let's try some other variogram models

 fit.circ <- variofit(parana.bin,c(5000,250),"circular",wei="cressie",nugget=275)
 fit.exp <- variofit(parana.bin,c(5000,250),"matern",wei="cressie",nugget=275)
 lines.variomodel(fit.circ,lty=4)
 lines.variomodel(fit.exp,lty=5)

#Check other models from help(cov.spatial)