Jack
I find Edzer notation confusing, since evryone I know
uses C0 for the bugget effect not the total sill of
the semi-variogram model.
The correlogram relationship is a theoretical one but
should hold provided the paricular gamma(h) is
calculated using all the same samples at the total
sill. According to Noel Cressie (Statistics for
Spatial Data) the Cauchy Schwartz inequality should
hold for every point on the calculated semi-variogram
subject to all samples being used at the lag.
You are probably getting greater than 1 because of
clustered sampling (?) or non-Normality in your data.
Isobel
--- jack webster [EMAIL PROTECTED] wrote:
Hi
Thank you, for replying, but as you know the
theorical
relation between variogram and correlogram
r(h) = (C0-gamma(h))/C0 does not holds for
estimates!
I compute correlogrm with
r^(h) = (C0^-gamma^(h))/C0^
(^ = estimate notation)
but in some lags r^(h) was grater than 1 !!
Sincelely: webster
===
--- Edzer J. Pebesma [EMAIL PROTECTED] wrote:
jack webster wrote:
hello,
I need a good estimator for the Correlogram,
under
2nd
stationary condition, and a SPLUS program for
computing it (if there is).
Sincerely: Webster
Jack, you can use the S-Plus function acf() to
compute
autocorrelations from one-dimensional data. As
you're
on this list, you probably want it for 2-D or 3-D.
For those
data you can compute variograms.
In S-Plus, library gstat (free; www.gstat.org) or
the S-Plus
SpatialStats module (commercial) are available for
computing sample variograms and modelling
variogram
functions.
The correlogram can be computed from a variogram
by
r(h) = (C0-gamma(h))/C0
with C0 the sill of the variogram and gamma(h) the
semivariance (model) value.
--
Edzer
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