If you want to avoid zero distances, what Edzer meant is to actually jitter the
points and then recalculate the distance matrix based on the new locations.
Replacing the zeros doesn't give you a valid distance matrix because it doesn't
account for having moved the two points with respect to all
Edzer,
Thanks for your help! I tried the first suggestion.., I replaced all
the zero's in the distance matrix with a different value.., but I still
have the same problem. I get negative eigenvalues. I tried various
constants, replacing the zero with positive numbers up to 5, with no
luck.
Keith,
indeed kriging usually fails when one or more point pairs have zero
distance. One solution in terms of distances would be to shift these
points a bit, such that no zero distances occur anymore. In terms of the
covariances, the solution would be to lower the corresponding
off-diagonal en
Roger,
Thanks for your response--as always (and I've lurked here a while), you were
very helpful.
There are three contexts I can think of in which two weight matrices would be
desirable:
1. For a gravity model, where each observation is a link between source region
and destination region, one s
On Tue, 11 Sep 2007, [EMAIL PROTECTED] wrote:
>
>> These days in GIS on may have to manipulate big datasets or arrays.
>>
>> Here I am on WINDOWS I have a 4Gb
>> my aim was to have an array of dim 298249 12 10 22 but that's 2.9Gb
>
Assuming double precision (no single precision in R), 5.8Gb.
>
>
On Mon, 10 Sep 2007, E. Anthon Eff wrote:
> Please forgive what may be a naive question. I'm working on a model that
> requires two different weight matrices, as in the "biparametric" model
> introduced by Brandsma and Ketellapper in 1979. I haven't had any luck
> finding a way to do this in R. An
Hello all,
First I would like to apologize if this question is inappropriate for
this list. I am new here, I found this list doing a web search and it
seemed like the members here would have knowledge in this area. If
there are more appropriate lists of forums for this question, I would
app
> These days in GIS on may have to manipulate big datasets or arrays.
>
> Here I am on WINDOWS I have a 4Gb
> my aim was to have an array of dim 298249 12 10 22 but that's 2.9Gb
It used to be (maybe still is?) the case that a single process could only
'claim' a chunk of max size 2GB on Windows.
These days in GIS on may have to manipulate big datasets or arrays.
What is the best way of tuning the R memory ?
or how can we not worry about the memory usage
i.e. could it that if it is needed it will swap on the disk (the system
I suppose)
That means declaring even more RAM than existing ...
