Hi,
I am sorry I say that I cannot get the same R^2 between in R/spgwr and SAM in
my data.
In R/spgwr
R^2: 0.972989;
AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 4668.92
Effective number of parameters (model: traceS): 435.7586;
Effective number of parameters (residual: 2traceS - traceS'S): 582.3581;
Sigma (residual: 2traceS - traceS'S): 2.437066;
Sigma (model: traceS): 1.927127;
Sigma (ML): 1.325501;
In SAM,
Coefficient of Determination : 0.696
Adjusted r-square (r?Adj): 0.693
Sigma: 20.058
Effective Number of Parameters: 10.002
Akaike Information Criterion (AICc): 4838.299
Correlation Coefficient (r): 0.834
F: 207.852
Here are my code:
PET.adapt.gauss <- gwr.sel(SPECIES_RI ~ PET, data=Environmental_variables,
coords=cbind(Environmental_variables$LONGX,
Environmental_variables$LATY),adapt=TRUE)
PET.gauss<- gwr(SPECIES_RI ~ PET, data=Environmental_variables,
coords=cbind(Environmental_variables$LONGX,
Environmental_variables$LATY),
gweight=gwr.Gauss,adapt=PET.adapt.gauss,hatmatrix=TRUE)
1 - (PET.gauss$results$rss/crossprod(scale(Environmental_variables$SPECIES_RI,
scale=FALSE)))
In SAM, I selecte "spatial Weighting Function"=gaussian, adaptive Spatial
Kernel, and compute Geographical Distances based on longitudinal coordinate(X)
and latitudinal coordinate(Y). I donot select method for AIC optimisation.
So I donot know where is wrong.
Thank you very much for your great helps.
ÔÚ2010-05-13 00:07:23£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>On Wed, 12 May 2010, Roger Bivand wrote:
>
>> On Wed, 12 May 2010, huangykiz wrote:
>>
>>> Hi, Is "adapt=TRUE"(spgwr) not the same as "adaptive Spatial
>>> Kernel"(SAM)?The result of "adaptive Spatial Kernel" may be better than
>>> fixed bandwidth. If I want to ues "adaptive Spatial Kernel" in spgwr, how
>>> to write the code?
>>
>> READ THE HELP PAGES!
>>
>> adaptive_proportion <- gwr.sel(...)
>>
>> result <- gwr(..., adapt=adaptive_proportion; ...)
>>
>> exactly as on the example om the help page:
>>
>> data(georgia)
>> g.adapt.gauss <- gwr.sel(PctBach ~ TotPop90 + PctRural + PctEld + PctFB +
>> PctPov + PctBlack, data=gSRDF, adapt=TRUE)
>> res.adpt <- gwr(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
>> PctBlack, data=gSRDF, adapt=g.adapt.gauss)
>> res.adpt
>>
>> Clear?
>
>I have now compared the same data in R/spgwr and SAM for effective number
>of parameters, sigma, and your questionable R^2, and they agree adequately
>when the kernel and the bandwidth are the same. Having the algorithm
>choose the bandwidth does obscure what is going on. You should use SAM if
>you prefer GUI and not needing to know how things work, and remember that
>GWR is a very doubtful approach for anything beyond exploring
>non-stationarity, its original motivation.
>
>>
>>>
>>> Thanks a lot.
>>>
>>> Cheers.
>>>
>>>
>>>> Hi,
>>>> I think that I use the same bandwidth and kernel. In SAM, I use "spatial
>>>> Weighting Function"=gaussian, adaptive Spatial Kernel, and compute
>>>> Geographical Distances based on longitudinal coordinate(X) and latitudinal
>>>> coordinate(Y). In spgwr, gweight is gwr.Gauss and adapt is TRUE.
>>>>
>>>> For example, this is my code:
>>>
>>>> PET.bw <- gwr.sel(SPECIES_RI ~ PET, data=variables,
>>>> coords=cbind(variables$LONGX, variables$LATY),adapt=TRUE)
>>>
>>>> PET.gauss <- gwr(SPECIES_RI ~ PET, data=variables,
>>>> coords=cbind(variables$LONGX, variables$LATY), bandwidth=PET.bw,
>>>> gweight=gwr.Gauss,adapt=TRUE,hatmatrix=TRUE)
>>>
>>> So where do you pass PET.bw to the gwr() function? adapt=TRUE will treat
>>> the adaptive proportion as 1, so include all data points. If you want to
>>> compare, use a fixed bandwidth in both, with no CV selection. Then you
>>> compare like with like.
>>>
>>> Note that your messages are *not* reaching the list, they must be sent to:
>>>
>>> r-sig-geo@stat.math.ethz.ch, not
>>>
>>> r-sig-geo-requ...@stat.math.ethz.ch
>>>
>>> You are not thinking carefully and are rushing into things and drawing
>>> wrong conclusions.
>>>
>>>>
>>>> Thanks a lot.
>>>>
>>>> Cheers.
>>>>
>>>>
>>>>
>>>> ÔÚ2010-05-12 20:28:47£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>>> On Wed, 12 May 2010, huangykiz wrote:
>>>>>
>>>>>> Hi,
>>>>>> One of SAM author ("Jos¨¦ Alexandre Felizola Diniz
>>>>>> Filho"<di...@icb.ufg.br>) say that they also base on GWR3 (the
>>>>>> Fotherigham book) and the data used within each kernel may be some
>>>>>> slight differences
>>>>>
>>>>> Naturally, if you are not using exactly the same kernel and bandwidth,
>>>>> you should not be surprised by differences in values. Please make sure
>>>>> that the bandwidth and kernel are the same and try again.
>>>>>
>>>>> Roger
>>>>>
>>>>>> Cheers
>>>
>>>
>>>
>>>
>>> ÔÚ2010-05-12 20:28:47£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>> On Wed, 12 May 2010, huangykiz wrote:
>>>>
>>>>> Hi,
>>>>> One of SAM author ("Jos¨¦ Alexandre Felizola Diniz
>>>>> Filho"<di...@icb.ufg.br>) say that they also base on GWR3 (the
>>>>> Fotherigham book) and the data used within each kernel may be some
>>>>> slight differences
>>>>
>>>> Naturally, if you are not using exactly the same kernel and bandwidth, you
>>>> should not be surprised by differences in values. Please make sure that
>>>> the bandwidth and kernel are the same and try again.
>>>>
>>>> Roger
>>>>
>>>>> Cheers.
>>>>>
>>>
>>>
>>>
>>>
>>>
>>> ÔÚ2010-05-12 15:27:58£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>> On Wed, 12 May 2010, huangykiz wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I am sorry I donot know how to install module spgwr from sourceforge (I
>>>>> can find it on the web
>>>>> http://r-spatial.cvs.sourceforge.net/viewvc/r-spatial/spgwr/R/gwr.R?view=log).
>>>>>
>>>>> So I use the code sketch to calculate quasi-global R2. The results are
>>>>> different between SAM and spgwr(Attached are the results ). The
>>>>> quasi-global R2 in R is 0.4515894, but in SAM is 0.696.
>>>>> This is my code:
>>>>>
>>>>> library(spgwr)
>>>>> Environmental_variables<-read.csv("Environmental_variables100.csv",header=TRUE)
>>>>> attach(Environmental_variables)
>>>>> region_PET.bw <- gwr.sel(SPECIES_RI ~ PET, data=Environmental_variables,
>>>>> coords=cbind(Environmental_variables$LONGX,
>>>>> Environmental_variables$LATY),adapt=TRUE)
>>>>> region_PET.gauss <- gwr(SPECIES_RI ~ PET, data=Environmental_variables,
>>>>> coords=cbind(Environmental_variables$LONGX,
>>>>> Environmental_variables$LATY), bandwidth=region_PET.bw,
>>>>> gweight=gwr.Gauss,adapt=TRUE,hatmatrix=TRUE)
>>>>> names(region_PET.gauss$SDF)
>>>>> region_PET.gauss$SDF$localR2
>>>>> 1 -
>>>>> (region_PET.gauss$results$rss/crossprod(scale(Environmental_variables$SPECIES_RI,
>>>>>
>>>>> scale=FALSE)))
>>>>>
>>>>> Thank you very much.
>>>>
>>>> SAM is closed source - ask them how they compute it. For spgwr, the code
>>>> is provided, so you can read it for yourself. For the record, the current
>>>> gwr() code in spgwr gives the same value as GWR3, which is also closed
>>>> source, and where the Effective number of parameters (model: traceS),
>>>> Sigma, and Residual sum of squares also agree. I suppose SAM has a
>>>> different understanding of GWR internals than the authors of the GWR book.
>>>>
>>>> Once again:
>>>>
>>>> Please *do* write to the R-sig-geo list rather than to me directly -
>>>> others can answer your question as well, perhaps better, and in a more
>>>> timely way than I can. In addition, threads in the list can be searched in
>>>> the archives, so others can avoid the same problem later.
>>>>
>>>> Please summarise to the list if this resolves the problem.
>>>>
>>>> Roger
>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> ÔÚ2010-05-12 01:16:18£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>>>> On Wed, 12 May 2010, huangykiz wrote:
>>>>>>
>>>>>>> Hi, I just need one for global, not *each* fit point. In this case, how
>>>>>>> can I select or do? Why in other software such as SAM(Spatial Analysis
>>>>>>> in Macroecology) just gives one R2?
>>>>>>
>>>>>> If you believe theirs, good luck! The authors of the GWR book have local
>>>>>> R^2 values in GWR3 and formulae that are wrong by their own admission in
>>>>>> private emails. The localR2 now agrees with the as-yet unreleased GWR4
>>>>>> from the GWR authors. How SAM can be "better", I don't know. What you
>>>>>> are suggesting is that the model fitted with fit points at data points
>>>>>> (but not at other fit points) might have a "quasi-global" R^2, based on
>>>>>> the RSS of the pooled fit. For the columbus case, that might be:
>>>>>>
>>>>>> 1 - (col.gauss$results$rss/crossprod(scale(columbus$crime,
>>>>>> scale=FALSE)))
>>>>>>
>>>>>> but I don't know whether this is in any way correct. I've added it as:
>>>>>>
>>>>>> Quasi-global R2:
>>>>>>
>>>>>> to the print output of a GWR model fitted with a hatmatrix, and have
>>>>>> committed it to sourceforge, project r-spatial, module spgwr. Arguably,
>>>>>> it ought to be adjusted by the ratio of degrees of freedom, but I don't
>>>>>> trust the DF either. Could you please check out spgwr from sourceforge
>>>>>> ,install it from source, and confirm that the "quasi-global R2" does the
>>>>>> same as SAM, or use the code sketch above to do the same, and report
>>>>>> back?
>>>>>>
>>>>>> Roger
>>>>>>
>>>>>>>
>>>>>>> Thanks a lot.
>>>>>>>
>>>>>>> Cheers,
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> ÔÚ2010-05-11 23:59:44£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>>>>>> On Tue, 11 May 2010, huangykiz wrote:
>>>>>>>>
>>>>>>>>> Hi,
>>>>>>>>>
>>>>>>>>> There are 49 localR2 in the results. Which one do I need? The code
>>>>>>>>> "look for localR2:" cannot run.
>>>>>>>>
>>>>>>>> Well, how many do you want? There is one for each fit point, they are
>>>>>>>> *local* R2. Please do try to grasp what GWR does - it fits one moddel
>>>>>>>> for *each* fit point.
>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thans a lot
>>>>>>>>>
>>>>>>>>> Cheers.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> ÔÚ2010-05-11 22:33:59£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>>>>>>>> On Tue, 11 May 2010, huangykiz wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi, OK. But I need it for compariation. In what some contexts to
>>>>>>>>>>> get it? May you tell me how to get it?
>>>>>>>>>>
>>>>>>>>>> library(spgwr)
>>>>>>>>>> data(columbus)
>>>>>>>>>> col.bw <- gwr.sel(crime ~ income + housing, data=columbus,
>>>>>>>>>> coords=cbind(columbus$x, columbus$y))
>>>>>>>>>> col.gauss <- gwr(crime ~ income + housing, data=columbus,
>>>>>>>>>> coords=cbind(columbus$x, columbus$y), bandwidth=col.bw,
>>>>>>>>>> hatmatrix=TRUE)
>>>>>>>>>> names(col.gauss$SDF)
>>>>>>>>>>
>>>>>>>>>> look for localR2:
>>>>>>>>>>
>>>>>>>>>> col.gauss$SDF$localR2
>>>>>>>>>>
>>>>>>>>>> But do not rely on it or use it for anything at all! Like all GWR,
>>>>>>>>>> it is most unreliable!
>>>>>>>>>>
>>>>>>>>>> Roger Bivand
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Thank you very much for your great helps
>>>>>>>>>>>
>>>>>>>>>>> Best regards.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> ÔÚ2010-05-11 18:28:44£¬"Roger Bivand" <roger.biv...@nhh.no> дµÀ£º
>>>>>>>>>>>> On Tue, 11 May 2010, huangykiz wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Dear professor Bivand,
>>>>>>>>>>>>>
>>>>>>>>>>>>> I am a strudent. I recently use GWR(Geographically weighted
>>>>>>>>>>>>> regression) model. May I ask you a question? There is not
>>>>>>>>>>>>> Coefficient of Determination in the results of GWR. How can I get
>>>>>>>>>>>>> it? What is the programs to get it?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Please address questions like this to the R-sig-geo list rather
>>>>>>>>>>>> than to me directly in future.
>>>>>>>>>>>>
>>>>>>>>>>>> The local R2 values are available in some contexts when running
>>>>>>>>>>>> gwr(), but are not well defined (neither in the GWR book nor in
>>>>>>>>>>>> implementations). I advise against their use - they are most
>>>>>>>>>>>> probably meaningless.
>>>>>>>>>>>>
>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>
>>>>>>>>>>>> Roger Bivand
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thank you very much for your any helps.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best regards.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Yong Huang
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> Roger Bivand
>>>>>>>>>>>> Economic Geography Section, Department of Economics, Norwegian
>>>>>>>>>>>> School of
>>>>>>>>>>>> Economics and Business Administration, 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
>>>>>>>>>> Economic Geography Section, Department of Economics, Norwegian
>>>>>>>>>> School of
>>>>>>>>>> Economics and Business Administration, 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
>>>>>>>> Economic Geography Section, Department of Economics, Norwegian School
>>>>>>>> of
>>>>>>>> Economics and Business Administration, 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
>>>>>> Economic Geography Section, Department of Economics, Norwegian School of
>>>>>> Economics and Business Administration, 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
>>>> Economic Geography Section, Department of Economics, Norwegian School of
>>>> Economics and Business Administration, 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
>Economic Geography Section, Department of Economics, Norwegian School of
>Economics and Business Administration, 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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