[STATSGRASS] questions regarding GRASS/gstat

[Edzer J. Pebesma]

Points outside the mask area can't hurt.

I can't help you without looking at your data,
mask, and command file.
--
Edzer

Thomas Adams wrote:

> Edzer,
>
> Thank you for your response. The models I have tried are: Spherical, 
> Exponential, and Power, nothing else. Poizot Emmanuel, in his 
> response, said that in his experience, one must not have points 
> outside the mask area. I have not tried this as yet. When I do I let 
> both you and Pouzot know what happened.
>
> Regards,
> Tom
>
>
> Edzer J. Pebesma wrote:
>
>>
>>
>> Thomas Adams wrote:
>>
>>> I have been attempting to use gstat within GRASS and have had 
>>> success up to a point. I am attempting to estimate precipitation 
>>> over a large river basin (~450000 sq km) from monthly (not mean 
>>> monthly) point values. I'm working with a Lambert Conic Conformal 
>>> grid region with a ~5 km grid spacing (the x & y spacings are 
>>> slightly different). My orginal dataset has nearly 500 data points, 
>>> but some were too close together (actually coincident points with 
>>> different IDs), so I wrote a program to eliminate points that were 
>>> too close, <7.5 km. I have increased this threshold several times, 
>>> to 50 km and still get a gstat error when I attempt the ordinary 
>>> kriging, which says that I have a "matrix library error: singular 
>>> matrix".
>>>
>>> I must be doing something wrong since my once nearly 500 station 
>>> locations is now ~100! One other note, is that gstat reports that 
>>> some of my stations lie outside my mask region (the river basin 
>>> boundary), but I don't see that this should be a problem, is it? 
>>
>>
>>
>> Once you end up with a singular covariance matrix, it is pretty hard
>> (and gstat does not attempt) to find out the reason why it occurs.
>> Possible reasons are (as you said): duplicate observations -- gstat
>> can catch them by taking their average, using something like:
>>
>> data(rainfall): 'rainfall', ... , average=1;
>>
>> other reasons may be the variogram model you use: the Gaussian
>> model often leads to singular models, and some authors (M. Stein)
>> argue not to use it at all; other models, such as linear-with-sill
>> should be avoided in 2D or 3D situations, but gstat does not check
>> this.
>>
>> Let me know if this didn't help.
>> -- 
>> Edzer
>>
>