Geographically weighted regression

Thank you for your answer.
?I tried to do the transformations before the calculation, but the 
Problem is still the same. I think the problem lies in the factor. But 
in my model I need the factor, because there is no linear influence of 
the year.
?
Is there any solution for this Problem?
?https://www.dropbox.com/s/fbhwsy3sd333ung/Example.zip?dl=0 The zip-file 
in the link contains a picture of str(Daten90) and str(Daten10) and also 
my data and R-skript. Var4 is log(Var2).
?

gwrGp <- gwr(formula=Ziel~ Var1 + Var3 + Var4, data = Daten90,

gwrGp <- gwr(formula=Ziel~ Var1 + Var3 + Var4, data = Daten90,

Christoph
?
?
?
?
The first step should be to look at

str(Daten90)
str(Daten10)

and if that doesn't solve the problem, then consider a reproducible
example, or at the very least posting the results of the above to this
list.

Sarah

On Fri, Feb 22, 2019 at 7:38 AM <f-c-b at web.de> wrote:

Dear all,

I am currently working out a geographically weighted regression, in which

90% of the data set the model should be calculated and for 10% of the values
to be predicted. For the prediction I use the function gwr.predict from the
package GWModel:

Erg<-gwr.predict(formula=Ziel~ as.factor(Var1) + log(Var2, base = exp(1))

+ Var3, data = Daten90,predictdata = Daten10,bw = bwG, kernel =
"gaussian",adaptive = FALSE, p = 2, theta = 0, longlat = FALSE)

I always get this error, although Daten10 and Daten90 have the same

structure:

Error in gwr.predict(formula = Ziel~ as.factor(Var1) + log(Var2, base =

exp(1)) + Var3, :

All the independent variables should be included in the predictdata.

Can you tell me what the problem with this code is?
Or is there any other way for a GWR and the prediction?

Thank you,
Christoph

--
Sarah Goslee (she/her)
http://www.numberwright.com

Hello Roger,
?
thank you for your answer.
?
I tried:

gwrGp <- gwr(formula=Ziel~ Var1 + Var3 + Var4, data = Daten90,

+ bandwidth = bwG, gweight = gwr.Gauss,hatmatrix = TRUE,
+ fit.points=Daten10, predictions=TRUE, fittedGWRobject=gwrG)
?
Now the prediction works, but when I give out the result, I get this error:

Error in sqrt(x$results$sigma2.b) :
  non-numeric argument to mathematical function
How can I solve this problem?

?
Thank you,
Christoph

Thank you for your answer.
?I tried to do the transformations before the calculation, but the
Problem is still the same. I think the problem lies in the factor. But
in my model I need the factor, because there is no linear influence of
the year.
?
Is there any solution for this Problem?
?https://www.dropbox.com/s/fbhwsy3sd333ung/Example.zip?dl=0 The zip-file
in the link contains a picture of str(Daten90) and str(Daten10) and also
my data and R-skript. Var4 is log(Var2).
?

I see:

gwrGp <- gwr(formula=Ziel~ Var1 + Var3 + Var4, data = Daten90,

+ bandwidth = bwG, gweight = gwr.Gauss,hatmatrix = TRUE,
+ fit.points=Daten10, predictions=TRUE)
Warning message:
In gwr(formula = Ziel ~ Var1 + Var3 + Var4, data = Daten90, bandwidth =
bwG, :
standard errors set to NA, normalised RSS not available

but using your gwrG object and:

gwrGp <- gwr(formula=Ziel~ Var1 + Var3 + Var4, data = Daten90,

+ bandwidth = bwG, gweight = gwr.Gauss,hatmatrix = TRUE,
+ fit.points=Daten10, predictions=TRUE, fittedGWRobject=gwrG)

I think the model matrix handling of the factor is OK. The problem in the
GWmodel::gwr.predict() approach is that the full model matrix approach is
used on the formula and data arguments, but not on predictdata, which is
coerced to data frame but never regularised by going through model.matrix,
hence the error message:

Browse[2]>
debug: if (any((inde_vars %in% names(predictdata)) == F)) stop("All the
independent variables should be included in the predictdata")
Browse[2]> inde_vars
[1] "Var12006" "Var12007" "Var12008" "Var12009" "Var3" "Var4"
Browse[2]> names(predictdata)
[1] "OBJECTID" "Rechtswert" "Hochwert" "Var1" "Var2"
[6] "Var3" "Ziel" "Var4" "coords.x1" "coords.x2"

Had the proper approach been used, the names would have been the same.

The relevant part of spgwr::gwr() in R/gwr.R is:

if (predictions) {
t1 <- try(slot(fit.points, "data"), silent=TRUE)
if (class(t1) == "try-error")
stop("No data slot in fit.points")
predx <- try(model.matrix(delete.response(mt), fit.points))
if (class(predx) == "try-error")
stop("missing RHS variable in fit.points")
if (ncol(predx) != ncol(x))
stop("new data matrix columns mismatch")
}

(lines 71-80)

which uses model.matrix() and uses try() to catch mis-matches.

Hope this helps,

Roger

Christoph
?
?
?
?
The first step should be to look at

str(Daten90)
str(Daten10)

and if that doesn't solve the problem, then consider a reproducible
example, or at the very least posting the results of the above to this
list.

Sarah

On Fri, Feb 22, 2019 at 7:38 AM <f-c-b at web.de> wrote:

Dear all,

I am currently working out a geographically weighted regression, in

which

90% of the data set the model should be calculated and for 10% of the

values

to be predicted. For the prediction I use the function gwr.predict from

the

package GWModel:

Erg<-gwr.predict(formula=Ziel~ as.factor(Var1) + log(Var2, base =

exp(1))

+ Var3, data = Daten90,predictdata = Daten10,bw = bwG, kernel =
"gaussian",adaptive = FALSE, p = 2, theta = 0, longlat = FALSE)

I always get this error, although Daten10 and Daten90 have the same

structure:

Error in gwr.predict(formula = Ziel~ as.factor(Var1) + log(Var2, base =

exp(1)) + Var3, :

All the independent variables should be included in the predictdata.

Can you tell me what the problem with this code is?
Or is there any other way for a GWR and the prediction?

Thank you,
Christoph

--
Sarah Goslee (she/her)
http://www.numberwright.com

--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: Roger.Bivand at nhh.no
https://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en