Hello,

I'm rather new on the list, so maybe a bit naive but educable.

I entered an emperical variogram, a matrix of initial parameter vectors, etc., and ran variofit. The results appear reasonable in terms of the nugget and range (see the structure below). But when I try plot(ghf) it fails:
> ghf
variofit: model parameters estimated by WLS (weighted least squares):
covariance model is: exponential
parameter estimates:
  tausq sigmasq     phi 
31.5821 40.1984  1.6064 
Practical Range with cor=0.05 for asymptotic range: 4.812493

variofit: minimised weighted sum of squares = 63.8023
> plot(ghf)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf

If I fix(ghf), this is what it looks like:

structure(list(nugget = 31.5820997227246, cov.pars = c(40.1983931188586, 
1.60644948107876), cov.model = "exponential", kappa = 0.5, value = 63.8022691526071, 
    trend = "cte", beta.ols = 22.5254647252906, practicalRange = 4.81249254536764, 
    max.dist = 9.95561289775773, minimisation.function = "optim", 
    weights = "cressie", method = "WLS", fix.nugget = FALSE, 
    fix.kappa = TRUE, lambda = 0.333333333333333, message = "optim convergence code: 0", 
    call = quote(variofit(vario = gho, ini.cov.pars = covpars, 
        cov.model = covmod, weights = "cressie"))), .Names = c("nugget", 
"cov.pars", "cov.model", "kappa", "value", "trend", "beta.ols", 
"practicalRange", "max.dist", "minimisation.function", "weights", 
"method", "fix.nugget", "fix.kappa", "lambda", "message", "call"
), class = c("variomodel", "variofit"))

Any clues?

Thanks!
Chris

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