Hi to all,

I have some questions about the ggwr logistic GWR function of spgwr package.
I'm trying to get the hat matrix to calculate the AIC for a comparison of
different models, but I get a message that the option "hat matrix=TRUE" is
disabled in ggwr. How can I get the effective number of parameters as GWR
book (pg.55) for a logistic model based on an adaptative gaussian kernel to
make this calculation? Is not possible to use AIC instead of CV as the
criteria for adaptative selection (ggwr.sel function)?

Beyond this questions I have a few others on ggwr function.


Is this model considered experimental? How can I get more information about
the implemented algorithm? It will change soon?

I`m using pgirmess package spatial correlograms of Moran`s I to check for
autocorrelation of residuals of global logistic and logistic gwr models
(response residuals). It`s correct or does it have another approach to check
spatial autocorrelation issues on global and local logistic
regression models?


My code is below and the measure to compare between models that I`m using is
C (area under curve) in Hmisc package. Thanks in advance for all comments
and suggestions.







library(spdep)

library(spgwr)

library(Hmisc)

fit = readShapePoints("C://Documents and Settings/Marcos Freitas/Meus
documentos/Doutorado/Area_Lages_trabalho/TESE_ESDA_LAGES/LOGISTICA/samples_floresta_01.shp"
, proj4string=CRS("+proj=utm +zone=-22 +datum=WGS84"), verbose = FALSE,repair
=FALSE)

head(fit)

bw = ggwr.sel(DEP_VAR ~ declividad + rug_norm + dist_riv + PTH_AR_MN +PTH_AR_SD
+ NP + LPI + MESH + GEOM_DC1, data=fit, coords=cbind(dados$Eastings, dados$
Northings), adapt=TRUE, gweight=gwr.Gauss, family=binomial(link="logit"),verbose
=TRUE, longlat=NULL, RMSE=FALSE, tol=.Machine$double.eps^0.25)

gwr.deg = ggwr(DEP_VAR ~ declividad + rug_norm + dist_riv + PTH_AR_MN
+PTH_AR_SD
+ NP + LPI + MESH + GEOM_DC1, data=fit, coords=cbind(fit$POINT_X, fit$
POINT_Y), gweight=gwr.Gauss, adapt=bw, family=binomial(link="logit"),longlat
=NULL, type=c("response"))

write.table (gwr.deg$SDF, file=paste("C://Documents and Settings/Marcos
Freitas/Meusdocumentos/Doutorado/Area_Lages_trabalho/TESE_ESDA_LAGES/LOGISTICA/lgwr_floresta_fit_reduzido"
, ".txt", sep=""))

pred_prob = fit$DEP_VAR - gwr.deg$SDF$response_resids

auc = somers2(pred_prob, fit$DEP_VAR, weights=NULL, normwt=FALSE, na.rm=TRUE
)

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