Dear Dr. Pebesma,

The exponential ST variogram did not solve the error. However, I 
manually set the lower bound for the nugget parameter to be equal to 1 
for both space and time. Even though the automatic optim() routine would 
estimate these to be 0, I decided to add a nugget variance to avoid the 
matrix singularity issue you mentioned. I am not sure why it would 
estimate a nugget of 0 (=no measurement error) on my data. Any idea why 
this could be?

Once I manually add a nugget variance, I now get this error when running 
the krigeST():

Error in cbind(v0, X) : number of rows of matrices must match (see arg 2)

The parameters of this krigeST function are unchanged, and the only 
thing that is modified is the variogram model described above.

Any help is much appreciated.

Thank you,
Francesco




On 8/3/2014 5:14 AM, Edzer Pebesma wrote:
> Dear Francesco, there are several reasons why this could happen. The
> first one I would look at is whether you have duplicate observations,
> meaning observations that share exactly the same spatial and temporal
> coordinate. Let me know if it helped,
>
> On 07/31/2014 05:45 PM, Francesco Tonini wrote:
>> Dear all,
>>
>> I am working with an hourly dataset of air temperature, recorded at ~200
>> stations over a relatively small area. I chose a space-time variogram
>> (e.g. sum-metric) to fit my data and am now trying to make predictions
>> over my same stations in order to fill NA (missing value) gaps. When
>> using the krigeST() function over daily aggregated data everything seems
>> to go smooth but when I use it at the original hourly resolution I
>> always get the following error:
>>
>> Error in chol.default(A)
>> the leading minor of order 68 is not positive definite
>>
>> I googled it and found that it is related to a matrix not being
>> completely positive-definite. However, I am not sure why this happens
>> and was wondering if any of you know a way of fixing this (a workaround
>> to avoid it).
>>
>> Thanks!
>> Francesco
>>
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>
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