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"


On 11/13/06, Roger Bivand <Roger.Bivand@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@nhh.no
>
>


-- 
Atenciosamente,

Raphael Saldanha
saldanha.plangeo@gmail.com
saldanha@plangeo.com.br

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