Dear all,
I am working with gwr function to predict precipitation distribution. I have
two datasets; first one is consisted of observation points (225 meteorological
stations), and the second one is DEM points used to obtain precipitation
predictions over it. I am using these functions:
bw1=gwr.sel(log10(PREC)~V1+V2+V3+V4+V5+V6,data=station,adapt=T)
gwr1<-gwr(log10(PREC)~V1+V2+V3+V4+V5+V6,data=station,adapt=bw1,se.fit=T,hatmatrix=TRUE)
gwr <-gwr(log10(PREC)~V1+V2+V3+V4+V5+V6, data=station, adapt=bw1, fit.points =
dem, predict=T, se.fit=T, fittedGWRobject=gwr1)
>From the independent variables, V1, V2 and V3 are continuous and V4, V5 and V6
>are categorical variables. Categorical variables has 4, 6 and 8 classes
>respectively. Stations and DEM points has coded values for these categorical
>variables. For example V5 has 6 land use classes.
My first question is: Can R program understand and analyses that categorical
values as codes instead of for ex. magnitude while making gwr analysis?
My second question is about dummy variables. I converted my categorical
variables to dummy variables and then I tried to make gwr. But while using
'fit.points=dem' gwr() gives error. I searched the internet, from some of
sources I read that dummy variables are not suitable for gwr analysis. Are
there anyone who has idea about using dummy variables in gwr?
Best regards,
Pinar
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