[R-sig-Geo] Line graph with over 70 lines plotted against time

[Jailos Lubinda]

Dear all,

I'm currently working on a dataset with 72 areas, recorded in quarterly 
dates (2000-2016). I want to visualise some variables using line graphs 
in R but have not found suitable tools in weeks now, as many examples 
are for creating a couple lines only. When I use ggplot with date 
(factor), it jumbles the dates making the lines uninterpretable. when I 
convert the date to "date", it breaks at Year points and doesn't give 
continuous lines.

Kindly help.

Thanks

Jailos

On 25/02/2019 08:50, Roger Bivand wrote:
> On Sun, 24 Feb 2019, f-c-b using web.de wrote:
>
>> 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?
>
> I am not looking over your shoulder. Always provide the code verbatim, 
> if I guess which mess you are in, I may get the wrong mess.
>
> Generally, I advise against GWR in all settings, it should only ever 
> be used for exploring data for non-stationarity.
>
> Roger
>
>>
>> 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 using 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
>> > > _______________________________________________
>> > > R-sig-Geo mailing list
>> > > R-sig-Geo using r-project.org
>> > > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>> >
>> >
>> >
>> > --
>> > 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 using nhh.no
>> https://orcid.org/0000-0003-2392-6140
>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>>
>>
>
>
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