R help - Dec 2006 - test of spatial dependence??

hello R-friends,

I am a R beginner and try to ask a basic question:

How to test the spatial dependence of a column of data? for example, I have
25 agricultural fields, and I measure the average slope (%) or pH for each
field. All I have is 25 numbers.

PS, could someone confirm that "spatial dependence" is equivalent to
"spatial correlation" or "spatial autocorrelation" or not.

Thank you very much.
XY

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In addition to the 25 numbers, I assume you have coordinates of each field.
  Otherwise, I don't understand what you are trying to do.  I think
ecologists like to use a test due to Mantel in this situation.
   
  The prefix "auto" means "self," of course, the idea being
that measurements of the same variable under different conditions are
correlated.  I guess this would be a case of "autodependence."  For
correlation versus dependence, check your intro stats book.
   
  de nada, 
  X'X 
   
  Farrar 
  

Xu Yuan <greenboy21@gmail.com> wrote:
  hello R-friends,

I am a R beginner and try to ask a basic question:

How to test the spatial dependence of a column of data? for example, I have
25 agricultural fields, and I measure the average slope (%) or pH for each
field. All I have is 25 numbers.

PS, could someone confirm that "spatial dependence" is equivalent to
"spatial correlation" or "spatial autocorrelation" or not.

Thank you very much.
XY

[[alternative HTML version deleted]]

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> How to test the spatial dependence of a column of data? for example
[...]
> All I have is 25 numbers.
If you don't have coordinates of your data, as I understand here,
there's nothing you can do, of course ...
If you have coordinates, you should compute the variogram -and/or the
spatial covariance- of your data and look if they are meaningful.
Hint: read a book about geostatistics.
> PS, could someone confirm that "spatial dependence" is equivalent
to
> "spatial correlation" or "spatial autocorrelation" or
not.
So, to me there is a light difference, but it's really "for advanced
users", and maybe questionable.

Bye.