Dear Dr. Edzer,
Yes, I read about the various options of fit.method in gstat manual. The
default value of fit.method for fitting variogram model to empirical
variogram is 7. That is, the number of pair of points divided by squares of
the distance, h. I refitted the model by setting fit.method =1 which is
weighted least squares so that it would be the same with default estimation
method of nls function. The comparison of the results differ greatly
despite using the same inital starting value. I am worried! Is there wrong
in my R script? Please help.

Thank you
Moshood


On Wed, Sep 4, 2013 at 4:40 AM, Edzer Pebesma <edzer.pebesma@uni-muenster.de
> wrote:

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> did you read the documentation of argument fit.method in function
> gstat::fit.variogram?
>
>
> On 09/04/2013 02:00 AM, Moshood Agba Bakare wrote:
> > Dear All, I tried to compare the result obtained by fitting the
> > spherical variogram model using fit.variogram and nls functions.
> > The large difference in the results is a great concern for me
> > knowing that the two functions use Weighted Least Squares (WLS)
> > approach for estimating parameters. The steps taken are as
> > follows:
> >
> > ## Fit empirical semivariogram using gstat empvar<-variogram(yield
> > ~ 1,canmod.sp,cutoff= 400,width = 25, Cressie=TRUE)
> >
> > # Fitting Spherical variogram model to sample variogram
> >
> > sph.var<- vgm(psill =130, model = "Sph", range = 65, nugget = 180)
> > sph.mod<-fit.variogram(empvar, model = sph.var) print(sph.mod)
> >
> > The result obtain from fitting the spherical model to sample
> > variogram is sph.mod model         psill         range 1   Nug
> > 230.917736411  0.0000000000 2   Sph 108.323055319 87.6889385431
> >
> > The non-linear least squares (NLS) approach use by default s
> > Gauss-newton algorithm in an iterative search process. I used the
> > initial values obtained from the empirical variogram above
> > (psill=130, range=65, and nugget=180) as starting values for the
> > iterative procedure.
> >
> > ## Define Spherical Variogram functions for NLS
> >
> > sph.vgram <- function(dist, range, psill, nugget){ dist <-
> > dist/range nugget + psill*ifelse(dist < 1, (1.5 * dist - 0.5 *
> > dist^3),1) }
> >
> > ## Fitting spherical with NLS
> >
> > fit.var <- nls(gamma~sph.vgram(dist,range,psill,nugget),data =
> > empvar, start=list(psill=130, range=65, nugget=180),trace=T)
> >
> > The result obtained from the nls fitting is
> >
> > Nonlinear regression model model:  gamma ~ sph.vgram(dist, range,
> > psill, nugget) data:  empvar psill       range      nugget
> > 90.7071423 342.1025007 278.9542178 residual sum-of-squares:
> > 1140.67875
> >
> > Number of iterations to convergence: 18 Achieved convergence
> > tolerance: 9.79837651e-06
> >
> > Could anyone explain why large difference in the two result? Is the
> > R script for fitting the NLS right? I am worried for having such
> > disparity. Thanks while looking forward to reading your suggestion,
> > comments and advice. Moshood.
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________ R-sig-Geo mailing
> > list R-sig-Geo@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >
>
> - --
> Edzer Pebesma
> Institute for Geoinformatics (ifgi), University of Münster
> Heisenbergstraße 2, 48149 Münster, Germany. Phone: +49 251
> 83 33081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de
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