Fantastic. Thank you so much.
On Wed, Sep 4, 2013 at 5:08 AM, Roger Bivand <roger.biv...@nhh.no> wrote:
> On Tue, 3 Sep 2013, Paul Bidanset wrote:
>
> Thank you very much for the example and the clarification. My hold out
>> test
>> is random. The vector provided by gw.adapt() allows me to see the
>> bandwidth
>> size for each point. Is there a way to see each regression point's
>> bandwidth size with the correct format you just showed me?
>>
>
> I wouldn't say "correct", rather feasible. Note that the local
> coefficients used for prediction use gSRDF1 data and positions relative to
> gSRDF2 postitions to calculate the coefficients, then the gSRDF2 covariates
> data to predict. Try:
>
> library(spgwr)
> example(georgia)
> par("ask"=FALSE)
> set.seed(1)
> s <- sample(nrow(gSRDF), 100)
> gSRDF1 <- gSRDF[s,]
> plot(gSRDF1, col="orange")
> gSRDF2 <- gSRDF[!(1:nrow(gSRDF) %in% s),]
> plot(gSRDF2, col="brown", add=TRUE)
>
> bwsel <- gwr.sel(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
> PctBlack, data=gSRDF1, adapt=TRUE, method="aic", longlat=TRUE)
> # adding longlat=TRUE to be sure, here the geographical coordinates are
> # known from the input data object, distances here in km
> bws <- gw.adapt(coordinates(gSRDF1), coordinates(gSRDF2), quant=bwsel,
> longlat=TRUE)
> bws
> # shows the adaptive bandwidths, which cannot be passed through the #
> bandwidth= argument, which is only for a single fixed value
>
> model1 <- gwr(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
> PctBlack, data=gSRDF1, adapt=bwsel, hatmatrix=TRUE, longlat=TRUE)
> model1
>
> PredictionsOfNewData <- gwr(PctBach ~ TotPop90 + PctRural + PctEld +
> PctFB + PctPov + PctBlack, data=gSRDF1, fit.points=gSRDF2, adapt=bwsel,
> prediction=TRUE, fittedGWRobject=model1, se.fit=TRUE, longlat=TRUE)
> PredictionsOfNewData
> plot(gSRDF2$PctBach, PredictionsOfNewData$SDF$pred)
> library(plotrix)
> plotCI(1:nrow(**PredictionsOfNewData$SDF), PredictionsOfNewData$SDF$pred,
> uiw=2*PredictionsOfNewData$**SDF$pred.se, xlab="test counties")
> points(1:nrow(**PredictionsOfNewData$SDF), gSRDF2$PctBach, pch=16)
> summary(gSRDF2$PctBach - PredictionsOfNewData$SDF$pred)
>
> Hope this helps,
>
> Roger
>
> PS.
>
> # the lm case
> lm1 <- lm(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
> PctBlack, data=gSRDF1)
> lmpred <- predict(lm1, gSRDF2, se.fit=TRUE)
> plot(gSRDF2$PctBach, lmpred$fit)
> summary(gSRDF2$PctBach - lmpred$fit)
> plotCI(1:nrow(gSRDF2), lmpred$fit, uiw=2*lmpred$se.fit, xlab="test
> counties")
> points(1:nrow(gSRDF2), gSRDF2$PctBach, pch=16)
> # maybe also see errorest in ipred
>
>
>
>>
>> On Tue, Sep 3, 2013 at 3:54 PM, Roger Bivand <roger.biv...@nhh.no> wrote:
>>
>> yOn Fri, 30 Aug 2013, Roger Bivand wrote:
>>>
>>> On Fri, 30 Aug 2013, Paul Bidanset wrote:
>>>
>>>>
>>>> Thank you. I'd like to subset into a specific county. Should there be
>>>>
>>>>> further partitioning from that level?
>>>>>
>>>>>
>>>>> No idea. Please re-create your scenario by subsetting georgia and the
>>>> coordinates to suit.
>>>>
>>>>
>>>> library(spgwr)
>>> example(georgia)
>>> gSRDF1 <- gSRDF[1:100,]
>>> gSRDF2 <- gSRDF[101:159,]
>>>
>>> bwsel <- gwr.sel(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov
>>> +
>>> PctBlack, data=gSRDF1, adapt=TRUE, method="aic")
>>>
>>> model1 <- gwr(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
>>> PctBlack, data=gSRDF1, adapt=bwsel, hatmatrix=TRUE)
>>> PredictionsOfNewData <- gwr(PctBach ~ TotPop90 + PctRural + PctEld +
>>> PctFB + PctPov + PctBlack, data=gSRDF1, fit.points=gSRDF2, adapt=bwsel,
>>> prediction=TRUE, fittedGWRobject=model1)
>>> plot(gSRDF2$PctBach, PredictionsOfNewData$SDF$pred)
>>>
>>> with the development version of spgwr on R-forge; with the released
>>> version the polygons of gSRDF2 cause an error. Note your confusion about
>>> adapt= in gwr(), if set as adapt=TRUE, this means adapt=1, so includes
>>> all
>>> the observations in the kernel, setting a very broad bandwidth. Never
>>> call
>>> gw.adapt(), it isn't a user-level function, but is exposed for exploring
>>> the inadequacies of GWR as a method. I would have appreciated an answer
>>> wrt. whether your held out test set is random or clustered, but here I've
>>> just subsetted the data in the simplest way.
>>>
>>> Roger
>>>
>>>
>>>
>>> Roger
>>>
>>>>
>>>>
>>>> On Fri, Aug 30, 2013 at 10:19 AM, Roger Bivand <roger.biv...@nhh.no>
>>>>> wrote:
>>>>>
>>>>> On Fri, 30 Aug 2013, Paul Bidanset wrote:
>>>>>
>>>>>>
>>>>>> Alrighty then!
>>>>>>
>>>>>>
>>>>>>>
>>>>>>> Thanks. Now make this your case by subsetting georgia in a way that
>>>>>> matches your case (all counties west of x?, random set?), and we may
>>>>>> be
>>>>>> getting closer. In the geographical partition, the fit points are all
>>>>>> a
>>>>>> long way from the data points, in the random case, they aren't grouped
>>>>>> in
>>>>>> the same way. You may also need to run the model twice, passing the
>>>>>> fitted
>>>>>> model (fit.points == data.points) through to the next stage, but I'm
>>>>>> unsure
>>>>>> about that.
>>>>>>
>>>>>> Roger
>>>>>>
>>>>>>
>>>>>> Say I create this adaptive bandwidth model using the original dataset
>>>>>>
>>>>>>> "georgia"
>>>>>>>
>>>>>>> coords = cbind(georgia$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.biv...@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.biv...@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" <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
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [[alternative HTML version deleted]]
>>>>>>>>>>>>>>
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>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [[alternative HTML version deleted]]
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> ______________________________**********_________________
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>>>>>>>>>>>> <h**ttps://stat.ethz.ch/****mailman/**listinfo/r-sig-geo<http://stat.ethz.ch/**mailman/**listinfo/r-sig-geo>
>>>>>>>>>>> <h**ttp://stat.ethz.ch/mailman/****listinfo/r-sig-geo<http://stat.ethz.ch/mailman/**listinfo/r-sig-geo>
>>>>>>>>>>> >
>>>>>>>>>>>
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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
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>> --
>>>>>>>>>
>>>>>>>>> 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
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>>
>>>>>> 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
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>> --
>>> 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
>>>
>>>
>>>
>>
>>
>>
> --
> 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
>
>
--
Paul Bidanset
(757) 412-9217
pbidan...@gmail.com
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