Dear Dr. Edzer,
My R script is not different from the previous mail. The only change I made
is to specify the fit.method = 1 which is weight least squares method going
by the gstat manual. The script is as follows

## Fit empirical semivariogram using gstat
empvar<-variogram(yield ~ 1,canmod.sp,cutoff= 400,width = 25)

## Spherical variogram model
sph.var<-vgm(psill=130, model="Sph", range=65, nugget=180)
sph.mod<-fit.variogram(empvar,model=sph.var,fit.method=1)
print(sph.mod)

The result obtain is


print(sph.mod)  model          psill         range
1   Nug 292.7856258929   0.000000000
2   Sph  81.4905260953 417.759633211




# 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 nls is


Formula: gamma ~ sph.vgram(dist, range, psill, nugget)

Parameters:
           Estimate   Std. Error  t value   Pr(>|t|)
psill   90.70714234   7.54388401 12.02393 2.0404e-08 ***
range  342.10250070  42.62964315  8.02499 2.1611e-06 ***
nugget 278.95421782   6.70590124 41.59832 3.2326e-15 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 9.36720447 on 13 degrees of freedom

Number of iterations to convergence: 18
Achieved convergence tolerance: 9.79837651e-06




On Wed, Sep 4, 2013 at 11:39 AM, Edzer Pebesma <
edzer.pebesma@uni-muenster.de> wrote:

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> On 09/04/2013 05:50 PM, Moshood Agba Bakare wrote:
> > 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.
>
> Instead of sharing emotions, you might as well share information.
>
> You don't provide a reproducible example, and you haven't shown your R
> script of the second attempt. In any case, I haven't seen you specify
> weights for nls(), and hence don't share your worries.
>
> >
> > Thank you Moshood
> >
> >
> > On Wed, Sep 4, 2013 at 4:40 AM, Edzer Pebesma
> > <edzer.pebesma@uni-muenster.de
> >> wrote:
> >
> > 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
> >>>>
> >
> >>
> >> _______________________________________________ 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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