[EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] En nombre de G. Allegri
> Enviado el: 21 August 2008 00:09
> Para: Paul Hiemstra
> CC: r-sig-geo@stat.math.ethz.ch
> Asunto: Re: [R-sig-Geo] variogram question
>
>> You spoke of more sophisticated methods of automatically choosing be
---Mensaje original-
De: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] En nombre de G. Allegri
Enviado el: 21 August 2008 00:09
Para: Paul Hiemstra
CC: r-sig-geo@stat.math.ethz.ch
Asunto: Re: [R-sig-Geo] variogram question
> You spoke of more sophisticated methods of automatically cho
> You spoke of more sophisticated methods of automatically choosing between
> models, what kind of methods did you have in mind?
Here's a list I gathered some time ago. Many of them are just buzzwords form me!
‧ Adjusted R-squared (Wherry 1931)
‧ Bootstrap (Efron 1979)
‧ Cross-validation (Stone
Hi,
There will certainly be situations when the SS method will select an
appropriate model. But there are also situations when it will not make a
well thought over decision. For example if there is not a lot of data on
short range variability the SS method will not be able to fit the model
th
> At this stage I do
> this by computing the sums of squares between the model and the sample
> variogram and choose the one with the smallest SS. This is a rather crude
> way of selecting between the models.
Thanks Paul for automap. I'm planning to try it in the next occasion.
What are the major
Hi Wesley,
Good to know that the package helped you.
A note the choice between the different variogram models. At this stage
I do this by computing the sums of squares between the model and the
sample variogram and choose the one with the smallest SS. This is a
rather crude way of selecting b
Dear Paul and the rest of the users who replied to my question,
Firstly many thanks for all your input, reading your emails this morning
improved my mood exponentially.
I have installed automap and am getting to know the program nicely, it is so
easy to run it is almost unfair. I do have one q
...in addition, any feedback on the package would be more than welcome!
Paul
Edzer Pebesma schreef:
In general: no, in special cases: yes.
Fitting variograms involves non-linear regression for most models
(Sph, Exp, Gau, ...) for the range parameter, so you need starting
values. Given the in
Hi,
The package Edzer was talking about, automap, can be downloaded from
http://intamap.geo.uu.nl/~paul/Downloads.html. It makes a few practical,
somewhat arbitrary assumptions about initial starting values.
- initial nugget is lowest semivariance found in the sample variogram
- initial sill
A possible extension of Edzer thoughts on this is to consider a initial
search for the non-linear range parameters.
For instance, take a grid of (20 say) values for this parameters within a
reasonable range for the given problem, e.g. between 0 and the maximum
distance.
Evaluate the objective func
In general: no, in special cases: yes.
Fitting variograms involves non-linear regression for most models (Sph,
Exp, Gau, ...) for the range parameter, so you need starting values.
Given the initial range, linear regression is sufficient to find the
nugget/sill component(s), as they are linear.
Dear r-sig-geo users,
I am currently analyzing some Lidar data we have collected over our study area.
I am interested in identifying the range of the semi-variogram as this value
will determine the width of pseudo-flight lines I intend to use to sample the
lidar data. Our point density is upwar
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