Dear Dr. Benedikt Gräler, Thank you for your advice. Now, I have found some directs to move on. Actually, I do not have enough background to analyze by myself. Based on your suggestions, I have plotted the variogram. I think there are daily and weekly seasonality. According to your suggestions, I am trying to model daily seasonality. It would be great if you can give me more advice. With Best Regards,Ankur
On Wednesday, May 17, 2017 6:10 AM, Dr. Benedikt Gräler
<b.grae...@52north.org> wrote:
Dear Ankur,
I see two issues from your plot:
i) there seems to be a seasonality in your data (maybe daily?) as the
sample variogram grows up to half the length of your temporal axis and
then drops again
ii) the starting values of your fitting routine are far off from what
can be deduced from the sample variogram. Hence, the numerical routines
have a hard time to identify the correct values. Additionally, the
parameters are of different orders of magnitude further complicating the
numerical fit. Manual re-scaling might help.
Try adding the parameter "scales=list(arrows=F)" for a visual assessment
of the empirical variogram; in your case
plot(var, wireframe=T, scales=list(arrows=F))
This reveals that the variogram indicates the same strength of
dependence for values 24 and 0 hours apart -> daily cycle? I'd suggest
to model the daily cycle first and then to model the spatio-temporal
variogram of the residuals.
HTH,
Ben
On 08/05/2017 06:33, Ankur Sarker via R-sig-Geo wrote:
> Hi,
>
> Can anyone suggest me the most appropriate variogram model to fit my data?
>
> I have tried three different models and results are too bad. Here is the
> comparison of different variogram models as attached.
>
> Thanks,
> Ankur
>
>
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Dr. Benedikt Gräler
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