[R-sig-Geo] GWR SDF
Roger Bivand
Roger.Bivand at nhh.no
Mon Nov 13 19:40:16 CET 2006
On Mon, 13 Nov 2006, Raphael Saldanha wrote:
> Thanks Roger! I'm just making some tests with the method
>
> Take a look below, this is what I have.
>
> > summary(lm(V03 ~ V04 + V05, as.data.frame(x)))
>
> Call:
> lm(formula = V03 ~ V04 + V05, data = as.data.frame(x))
>
> Residuals:
> Min 1Q Median 3Q Max
> -5.909 -3.855 -3.380 -2.694 185.091
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 5.9086 1.9982 2.957 0.00324 **
> V04 0.8069 0.3342 2.415 0.01607 *
> V05 0.1833 0.3356 0.546 0.58524
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Residual standard error: 16.84 on 558 degrees of freedom
> Multiple R-Squared: 0.965, Adjusted R-squared: 0.9649
> F-statistic: 7701 on 2 and 558 DF, p-value: < 0.00000000000000022
>
> > res.adpt
> Call:
> gwr(formula = V03 ~ V04 + V05, data = x, adapt = x.adapt.gauss,
> hatmatrix = TRUE)
> Kernel function: gwr.gauss
> Adaptive quantile: 0.1087359 (about 61 of 561)
> Summary of GWR coefficient estimates:
> Min. 1st Qu. Median 3rd Qu. Max. Global OLS
> X.Intercept. -16.0400 0.7464 2.7780 7.1090 42.5200 5.9086
> V04 -0.5183 0.6884 0.8273 1.4080 3.7480 0.8069
> V05 -2.6950 -0.4328 0.1553 0.2978 1.4020 0.1833
> Number of data points: 561
> Effective number of parameters: 37.25909
> Effective degrees of freedom: 523.7409
> Sigma squared (ML): 240.1169
> AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 4723.854
> AIC (GWR p. 96, eq. 4.22): 4692.986
> Residual sum of squares: 134705.6
>
> > names(res.adpt$SDF)
> [1] "sum.w" "X.Intercept." "V04" "V05" "gwr.R2"
> "X1" "X2" "X3"
> [9] "coord.x" "coord.y"
In order: sum of weights, three local coefficient estimates, the local
R-square, three local coefficient standard errors, and the data point
coordinates. I was puzzled that you said you included V06, V07, V08, but
got names V04, V05, but understand that that was just an example.
>
>
> On 11/13/06, Roger Bivand <Roger.Bivand at nhh.no> wrote:
> >
> > On Mon, 13 Nov 2006, Raphael Saldanha wrote:
> >
> > > Help!
> > >
> > > In the SDF results, what exactly means these fields:
> > >
> > > sum_w
> > > X.Intercept
> > > V04
> > > V05
> > > X1
> > > X2
> > > X3
> >
> > The spgwr package is not really intended to help people use GWR, which is
> > not generally accepted as a technique of analysis (because it forces
> > coefficients to co-vary), though it can be used for exploration. It is
> > rather a toolbox for examining the method. For this reason, not much
> > effort has been put into things like names. You would have to say what all
> >
> > the names are in this case:
> >
> > names(res.adpt$SDF)
> >
> > The v04, V05 look odd, but without seeing all the names, it is difficult
> > to tell. The X1-X3 are probably three of the local standard errors. s_w is
> > the sum of weights at that point, and X.Intercept is the local intercept
> > estimate.
> >
> > What happens when you run a regular regression? Do any of the coefficients
> > disappear (any collinear variables)?
> >
> > Roger
> >
> > >
> > > I'm using this code:
> > >
> > > > x <- readShapePoly("domicilio_3136702.shp", IDvar = "ID_")
> > > > x.adapt.gauss <- gwr.sel(V03 ~ V06 + V07 + V08, data=x, adapt=TRUE)
> > > > res.adpt <- gwr(V03 ~ V06 + V07 + V08, data=x, adapt= x.adapt.gauss,
> > > hatmatrix = TRUE)
> > >
> > >
> >
> > --
> > 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.Bivand at 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.Bivand at nhh.no
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