On Tue, 22 Jun 2010, Pinar Aslantas Bostan wrote:
Dear Roger,
Thank you for your mail. I tried lm() and it doesn't resulted with negative
values. Also gwr() doesn't give negative values if I don't use fit.points.
OK. What this means then is that for fit.points some local coefficients
with your chosen adaptive proportion and chosen kernel, and with the
values of Z, V1, and V2 (are V1 and V2 coordinates - is a GW trend model a
good idea?) are driving the predictions negative. Could you try this with
different adaptive proportion values to see whether the predictions stay
non-negative? Are the relationships between PREC and Z, V1, and V2 really
linear? Can you fit a gam() and look at the linearity of the fits? Would
taking log(PREC) help with the linearity (as well as with bounding the
model)? Do you have any resource persons at your university? Do you have
access to Chris Lloyd's nice 2006 book on local models:
http://www.crcpress.com/product/isbn/0415316812
Hope this helps,
Roger
bw1=gwr.sel(PREC~Z+V1+V2,station,adapt=T)
xx1<-gwr(PREC~Z+V1+V2,station,adapt=bw1,se.fit=T,hatmatrix=TRUE)
gwrx<-xx1$SDF
min(gwrx$pred)
[1] 311.189
max(gwrx$pred)
[1] 1700.559
But if I use fit.points to predict precipitation on a grid, then it gave
negative values.
x1 <-gwr(PREC~Z+V1+V2,station,adapt=bw1, fit.points = dem, predict=T,
se.fit=T, fittedGWRobject=xx1)
gwrres<-x1$SDF
min(gwrres$pred)
[1] -1126.052
max(gwrres$pred)
[1] 2104.136
You mentioned about limiting the design of the model, so that it doesn't give
negative values. Do you have any idea about that, how can I perform this?
Best regards,
Pinar
Alinti Roger Bivand <roger.biv...@nhh.no>
On Mon, 21 Jun 2010, Pinar Aslantas Bostan wrote:
Dear Roger,
Maybe you can remember, last week I asked some questions about gwr
analysis and you helped me. In order to make refresh, I want to summarize
my problem. I am trying to make gwr analysis to predict precipitation
distribution on a DEM. I have two datasets; first one is 'station'
consists of 225 prec. measurements and 3 independent variables (Z, V1,
V2). The second one is 'DEM' dataset which has 31203 # of pixels. Firstly
I tried with using SPDF (SpatialPointsDataFrame) and obtained predicted
precipitations on DEM. But when I plot the predicted precipitation values,
I saw a lot of negative values which is impossible to get such a values.
No, this is just a weighted linear model. Unless you limit it by design,
all such linear models will happily predict out of domain. Consider how you
might by design limit the response to non-negative values.
I thought that this was caused because I used point data sets (SPDF) for
grid.
Why would you think that? All you need to provoke this is a slightly
unfortunate placing of the met stations (all in west and falling trend
eastwards beyond the observed stations). Did the regular lm() fit also
predict negative values (very likely yes).
This isn't a GWR problem, it's more general. Fix it for lm() first.
Roger
Then I tried with using SGDF (SpatialGridDataFrame). At this time, gwr()
resulted with an error message "new data matrix rows mismatch". You told
me that 'The error message is generated when the number of columns in the
matrix of X variables is not the same in data and and fit.points.' I gave
some information about datasets below.
class(station)
[1] "SpatialPointsDataFrame"
attr(,"package")
[1] "sp"
names(station)
[1] "PREC" "Z" "V1" "V2"
class(dem)
[1] "SpatialGridDataFrame"
attr(,"package")
[1] "sp"
names(dem)
[1] "Z" "V1" "V2"
'PREC' is the observations and I want to get predictions of them on 'dem'.
I checked the str() and variables are stored in the same way.
bw=gwr.sel(PREC~Z+V1+V2,data=station,adapt=T)
xx1<-gwr(PREC~Z+V1+V2,station,adapt=bw,hatmatrix=TRUE)
x1 <-gwr(PREC~Z+V1+V2,data=station,adapt=bw, fit.points = dem, predict=T,
se.fit=T, fittedGWRobject=xx1)
Error in gwr(PREC ~ Z + V1 + V2, data = station, adapt = bw, fit.points =
dem, :
new data matrix rows mismatch
If I wrote 'predict=F' then gwr() works but gives only sum.w, coefficients
and localR2.
I tried to run gwr() under debug but I didn't understand the output.
I really don't understand the problem and need help.
Best regards,
Pinar
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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: roger.biv...@nhh.no
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--
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: roger.biv...@nhh.no
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