Anand,
I could not open your .grd file in R, I think the .gri file is missing.
However, looking at it in a text editor, and comparing with your R-script,
it seems something went wrong with your projections. Your .csv-file has
LatLong coordinates, but you are setting the projection to "+proj=utm
+zone=40 +south +datum=WGS84 +units=m +no_defs +ellps=WGS84
+towgs84=0,0,0", the same as you seem to have in your prediction raster.
But you need to reproject your observations in this case.
# First set the right projection:
proj4string(carbon_seq) <- CRS("+init=epsg4326") # epsg-code for latlong
# Then transform
carbon_seq = spTransform(carbon_seq, CRS("+proj=utm +zone=40 +south
+datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"))
If you do this, your prediction grid should overlap your observations, and
the colors should reflect your color ramp. What you essentially did before
was to make a prediction thousands of km away from your observations, and
then all values will be the same.
Best wishes,
Jon
On 8/4/2015 12:48 PM, Anand Sookun wrote:
Dear Jon and Roman
Thanks for the reply.
Im attaching the code.
You may be right that the variogram may be problematic but it fitted
well visually.
I think also that the prediction grid was not well created. I wiould be
grateful to have to hints.code how to create a useful grid and also for
the spplot or other alternatives
I can send more info if required.
Cheers
Anand
On Tue, Aug 4, 2015 at 1:39 PM, Jon Skoien <jon.skoien at jrc.ec.europa.eu
<mailto:jon.skoien at jrc.ec.europa.eu>> wrote:
Hi,
You have attached some data, but we still dont know exactly what you
do with the data (do you use the logarithm, how many bins, did you
project the data set...?). However, having had a quick look using
the LatLong coordinates (not really recommended), I can see that the
variogram from this data set is not particularly well defined, and
it could well be that you fitted a model with pure nugget effect. In
that case you will get the same prediction everywhere and no color
differences.
Have a look at
and particularly the summary of var1.pred.
You could also plot the variogram of the data together with your
model, vmf, and see how they fit.
I am not sure what you mean by not being able to create a prediction
grid with data included. Do you mean that you want a grid which
exactly overlays some of the data points? Matching one point should
not be difficult, 2 and 3 should be possible, but you might get some
problems with precision (FAQ 7.31).
Best wishes,
Jon
On 8/3/2015 5:30 PM, Anand Sookun wrote:
Hi
Thank you Roman for your kind response.
The data is as attached which is part of the whole dataset.
My other problem is that im not able to create an appropriate
prediction grid with some data included.
The codes which failed to show colour ramps are
map <- krige(logcarbseq ~ 1, locations = carbon_seq, newdata =
map,model = vmf)
str(map)
print(spplot(map, "var1.pred", asp=1,
col.regions=bpy.colors(64),main="OK prediction, carbon"))
print(spplot(map, "var1.var",col.regions=cm.colors(64),asp=1,
main="OK prediction variance, carbon"))
I look forward to hopefully get some solutions.
Thanks again and warm regards
Anand
On Mon, Aug 3, 2015 at 4:09 PM, Roman Lu?trik
<roman.lustrik at gmail.com <mailto:roman.lustrik at gmail.com>> wrote:
What is the variability of the plotted variable?
Cheers,
Roman
On Sun, Aug 2, 2015 at 11:03 AM, Anand Sookun
<<mailto:asookun at gmail.com>asookun at gmail.com
<mailto:asookun at gmail.com>> wrote:
Hi
I have kriged my data but the map shows uniform colours -
no gradual
contour colour plot on map
code: print(spplot(k40, "var1.pred", asp=1,
col.regions=bpy.colors(64),main="OK prediction, log-ppm Zn"))
thnaks for any help
Anand Sookun
--
-------------------------------------------------------
*Anand Sookun (**MPhil. **PhD Candidate, University of
Mauritius)*
*Director cum Freelance Consultant*
*Soft Sustainable Services (Mauritius)*
*Research and Consultancy*
Website: http://sustain-ability.webs.com/
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