Paul, Luis,
I suspect that your speculations are completely wrong-headed. Please
provide a reproducible example with a built-in data set, so that there is
at least minimal clarity in what you are guessing. Note in addition that
GWR as a technique should not be used for anything other than exploration
of possible mis-specification in the underlying model with the given data,
as patterning in coefficients is induced by GWR for simulated covariates
with no pattern.
Roger
On Fri, 30 Aug 2013, Luis Guerra wrote:
Thank you Luis. When calibrating the adaptive model, using adapt=t in the
bandwidth selection created the proportion you speak of, which then allowed
me to create a bandwidth matrix using gwr.adapt. However, this has not
worked for me with holdout samples. Have you had success in this regard?
Now I get what you mean. Let's show an example:
bw <- gwr.sel(var ~ var1, data=yourdata, adapt=TRUE)
m <- gwr(var~var1, data=yourdata, adapt=bw, fit.points=newdata)
So an adaptative bandwidth (bw) is calculated based on"yourdata", while you
are fitting "newdata" later on using that previously found bw. I had not
thought about it previously. Let's see whether someone else can help you
(us).
I do not know the intended influence of these "fit.points". I would think
that new localized regressions are not calculated, as we're testing the
model and previous data points' ability to predict for these new ones, but
I could be wrong. My current method, however, is producing much poorer
results with the holdouts, which I am fairly sure is related to my
inability to incorporate the new points necessary bandwidths.
Coming back to the previously created example, imagine that "newdata" is a
single point that you want to fit. Imagine now that "yourdata" is a sample
with 1000 cases. Then you are getting 1000 models with 1000 different
intercepts and 1000 different beta values to adjust var1, rigth? Which of
all these parameters do you use for fitting "newdata"? And something else,
what would happen with "newdata" if it is enough far away from "yourdata"
and we would be using a fixed bandwidth?
On Aug 29, 2013 8:56 PM, "Luis Guerra" <luispelay...@gmail.com> wrote:
Dear Paul,
I am dealing with this kind of problems right now, and if I am not wrong,
when you want to apply an adaptative bandwidth, you should introduce a
value for the "adapt" parameter instead of for the "bandwidth" parameter.
This value will be between 0 and 1 and indicates the proportion of cases
around your regression point that should be included to estimate each local
model. So depending on the amount of points around each case, the model
will use a different bandwidth for each point to be fitted.
Related to your question, do you know what is the influence of the data
introduced in the "data" parameter to the data to be fitted (introduced in
the "fit.points" parameter)? I mean, you have to obtain new local models
(one for each point to be fitted), so I do not understand whether the
"data" parameter is used somehow...
Best regards,
Luis
On Fri, Aug 30, 2013 at 1:26 AM, Paul Bidanset <pbidan...@gmail.com>wrote:
Hi Folks,
I was curious if anyone has had experience applying an SPGWR model with
an
adaptive bandwidth matrix to a holdout or validation sample. I am using
the
"fit.points" command, which does not seem to allow for a new bandwidth
calibrated around the holdout samples XY coordinates. Any direction would
be greatly appreciated. I am also open to other viable methods.
Cheers,
Paul
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Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: roger.biv...@nhh.no
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