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.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]] >>>>>>>>> >>>>>>>>> ______________________________******_________________ >>>>>>>>> R-sig-Geo mailing list >>>>>>>>> R-sig-Geo@r-project.org >>>>>>>>> https://stat.ethz.ch/mailman/******listinfo/r-sig-geo<https://stat.ethz.ch/mailman/****listinfo/r-sig-geo> >>>>>>>>> <https://**stat.ethz.ch/mailman/****listinfo/r-sig-geo<https://stat.ethz.ch/mailman/**listinfo/r-sig-geo> >>>>>>>>> > >>>>>>>>> <https://**stat.ethz.ch/**mailman/listinfo/**r-sig-geo<http://stat.ethz.ch/mailman/listinfo/**r-sig-geo> >>>>>>>>> <h**ttps://stat.ethz.ch/mailman/**listinfo/r-sig-geo<https://stat.ethz.ch/mailman/listinfo/r-sig-geo> >>>>>>>>> > >>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>> [[alternative HTML version deleted]] >>>>>>>> >>>>>>> >>>>>> ______________________________******_________________ >>>>>> R-sig-Geo mailing list >>>>>> R-sig-Geo@r-project.org >>>>>> https://stat.ethz.ch/mailman/******listinfo/r-sig-geo<https://stat.ethz.ch/mailman/****listinfo/r-sig-geo> >>>>>> <https://**stat.ethz.ch/mailman/****listinfo/r-sig-geo<https://stat.ethz.ch/mailman/**listinfo/r-sig-geo> >>>>>> > >>>>>> <https://**stat.ethz.ch/**mailman/listinfo/**r-sig-geo<http://stat.ethz.ch/mailman/listinfo/**r-sig-geo> >>>>>> <h**ttps://stat.ethz.ch/mailman/**listinfo/r-sig-geo<https://stat.ethz.ch/mailman/listinfo/r-sig-geo> >>>>>> > >>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> -- >>>>>> >>>>> 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 [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
