Nugget from variogram.fit vs GLS

1.30231/(1.413246+1.30231)

Hi All,

I'm verifying coefficients generated with GSTAT kriging with external
drift using GLS. The coefficients match absolutely perfectly. The range
also matches perfectly. But there is a big difference in what GLS gives
as the output for the nugget. Can anyone explain why this is?

Zev

###### KED

# here's where I fit the variogram and the fit output

v.fit = fit.variogram(v, vgm(1.5, "Exp", 20, 0.5))

v.fit

  model    psill     range
1   Nug 1.302310 0.0000000
2   Exp 1.413246 0.8098587

# the fit is used in a KED model
g = gstat(formula=formula(MODEL), data = so2, model = v.fit)

# here I get the coefs (these coefs match those from GLS below perfectly)
UKcoef<-predict(g, lattice, BLUE=c(TRUE,TRUE))

###### GLS

# use the same parameters as above and fix them

nug<-v.fit[1,2] # this is 1.302
sill<-v.fit[1,2]+v.fit[2,2]
range<-v.fit[2,3]

theGLS<-gls(formula(so2FinalAll), data=so2, na.action=na.omit,
correlation=corExp(c(range, nug/sill ),
    form=~X+Y, nugget=TRUE,fixed=TRUE), method="ML")

summary(theGLS)

Generalized least squares fit by maximum likelihood
  Model: formula(so2FinalAll)
  Data: so2
       AIC      BIC    logLik
  578.6551 593.7083 -284.3275

Correlation Structure: Exponential spatial correlation
 Formula: ~X + Y
 Parameter estimate(s):
    range    nugget
0.8098587 0.4795740

# note the nugget of 0.479 vs the 1.302 above.