Holdout Sampling Adaptive Bandwidth SPGWR

Thank you. I'd like to subset into a specific county. Should there be
further partitioning from that level?

On Fri, Aug 30, 2013 at 10:19 AM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

On Fri, 30 Aug 2013, Paul Bidanset wrote:

 Alrighty then!

Thanks. Now make this your case by subsetting georgia in a way that
matches your case (all counties west of x?, random set?), and we may be
getting closer. In the geographical partition, the fit points are all a
long way from the data points, in the random case, they aren't grouped in
the same way. You may also need to run the model twice, passing the fitted
model (fit.points == data.points) through to the next stage, but I'm unsure
about that.

Roger

Say I create this adaptive bandwidth model using the original dataset
"georgia"

coords = cbind(georgia$x, georgia$y)
bwsel <- gwr.sel(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
PctBlack, data=georgia, adapt=TRUE, coords, gweight=gwr.Gauss, method =
"aic" )
bw1 <- gw.adapt(coords, coords, quant=bwsel)
model1 <- gwr(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
PctBlack, data=georgia, bw=b1, coords, hatmatrix=T)
model 1

Suppose I receive an updated data set (same dependent and independent
variables) and I wish to test the above model1's ability to predict the
dependent variable of these new data points. If this were a basic lm
regression in R, I would use the "predict()" command. I wish to better
understand how I would do so using a GWR model. I found the below
procedure, but I would like to know first if it is capable accomplishing
this task, and secondly, if I am specifying it correctly. It seems to me
that this procedure, as it stands, doesn't take into account the
appropriate bandwidths for the new data, say, "georgiaNewData"

PredictionsOfNewData  <- gwr(PctBach ~ TotPop90 + PctRural + PctEld +
PctFB
+ PctPov + PctBlack, data=gSRDF, adapt=TRUE, gweight=gwr.Gauss, method =
"aic",  bandwidth=bw1,
predictions=TRUE, fit.points=georgiaNewData)
PredictionsOfNewData

Thanks in advance for guidance and insight...


On Fri, Aug 30, 2013 at 9:01 AM, Roger Bivand <Roger.Bivand at nhh.no>
wrote:

 Provide a reproducible code example of your problem using a built in data

set. No reproducible example, no response, as I cannot guess (and likely
nobody else can either) what your specific misunderstanding is. Code
using
for example the Georgia data set in the package. You seem to be assuming
that you understand how GWR works, I don't think that you do, so you have
to show what you mean in code.

Roger


On Fri, 30 Aug 2013, Paul Bidanset wrote:

 Roger,

I think all I would like to know is if it is possible to apply a
calibrated
GWR model to a hold-out sample, and if so, what the most accurate way to
do
so is. I understand the pitfalls of GWR but would like to learn as much
as
I can before progressing to the next spatial methodology I learn in R.


On Fri, Aug 30, 2013 at 3:37 AM, Roger Bivand <Roger.Bivand at nhh.no>
wrote:

 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" <luispelayo84 at 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 <pbidanset at 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.Bivand at nhh.no

 --

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.Bivand at nhh.no

--
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.Bivand at nhh.no