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$SDF) > [1] "sum.w" "X.Intercept." "V04" "V05" "gwr.R2" > "X1" "X2" "X3" > [9] "coord.x" "coord.y"
In order: sum of weights, three local coefficient estimates, the local R-square, three local coefficient standard errors, and the data point coordinates. I was puzzled that you said you included V06, V07, V08, but got names V04, V05, but understand that that was just an example. > > > On 11/13/06, Roger Bivand <[EMAIL PROTECTED]> wrote: > > > > On Mon, 13 Nov 2006, Raphael Saldanha wrote: > > > > > Help! > > > > > > In the SDF results, what exactly means these fields: > > > > > > sum_w > > > X.Intercept > > > V04 > > > V05 > > > X1 > > > X2 > > > X3 > > > > The spgwr package is not really intended to help people use GWR, which is > > not generally accepted as a technique of analysis (because it forces > > coefficients to co-vary), though it can be used for exploration. It is > > rather a toolbox for examining the method. For this reason, not much > > effort has been put into things like names. You would have to say what all > > > > the names are in this case: > > > > names(res.adpt$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: [EMAIL PROTECTED] > > > > > > > -- 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: [EMAIL PROTECTED] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo