- To clear things up,

I want to calculate the variance of values within an area (20x20m).

So you calculate the semi-variogram value from each point to every other

point in that area, you add up all the terms and divide by the number of

terms/pairs.

I think it's called a "block to block" variogram average, not sure.

A link to an illustration:

http://www.houlding.net/simon/DVEpaper/DVEfig03.htm

As I explained, I'm looking for an easy way to estimate the gamma(V,V)

value.

I used matlab to code for the distances between pairs but, it takes so

much time compared to geoR or gstat that I doubt I'm working correctly or

these packages are optimized in some way to cut back on calculation times.

Bottom line is that I don't want to spent to much time coding stuff and

more time doing the actual analysis.

Koen...

> Hi,

--

>

> what do you mean by average semi-variance? The semivariance at any lag is

> always going to be the average of the semivariance between many lag pairs

> (the exact number depends on sample size, spacing etc). As far as I am

> aware

> R calculates the average semivariance for each lag and presents this as

> the

> "experiemental semivariogram". Other softwarre provide the variogram

> cloud,

> the values of individual pair comparisons, from which the experimental

> variagram is averaged,

>

> hope this helps

>

> Benjamin

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Hard one without the mathematical writing, I looked it up and you refer to

> gamma_Y(x,x') = 1/|B|^2 \sum_{w in B} \sum_{w' in B} gamma_Z(x+w,x'+w')

>

> stays a biproduct, which would more or less (or more less) allow to infer

> gamma_Z from gamma_Y. However you don't seem to need relation. You can go

> along with gamma_Y only.

following calculations I presume (end of the page):

http://uk.geocities.com/drisobelclark/PG1979/Chapter_3/Part2.htm

> The first problem is that you measure according to equation (2) but you

It could do the trick, but then again... as someone asked before is it

> want

> to estimate according to equation (1). Anyway (2) should well approximate

> (1).

worth the effort, considering the pretty dense sampling grid in the

plot/block maybe this isn't the best way to approach the problem!?

> The second problem is: The variogram of Y should be very smooth in the

Correct on that one.

> origing, but it is most sure not Gaussian.

Or, I've got data that is highly skewed (LAI, leaf area indexes) and other

data that isn't (that much, reflectance/albedo measurements). So getting a

variogram in the first place has horror qualities (transforming,

backtransforming and that mess) even if I do the actual fitting on all the

data from multiple plots/blocks.

Thanks for all the input,

Koen.

--

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