Hi Everybody,
I was wondering if anyone knows if there is a R-package or r-Script that
can be used to calculate the Palmer Drought Severity Index?
Best regards,
Swen
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Dear All,
does anyone maybe know a R script or a software tool that transforms a
set of texture rasters (sand , silt , caly) into one classified raster
according the German triangle ‘‘Bodenkundliche Kartieranleitung 1994. I
know that SAGA GIS does have such a function but I think the results ar
(X_01_sum,
filename=paste(Path,"SoilMoisture_RM_Mean_",i,sep=""), "ascii")
}
The script works, but it takes more than 30 min until the grids are
precessed. Does anyone know of a faster way of meaning (summing) those
grids and exporting them as a asci again?
Best
Dear Lydon,
I also had some trouble with stacking Layers in the Raster package. Try
to use a 32 bit version of R. Sounds weired but in my case the stacking
with the Raster package was much faster using the Raster Package on a 32
bit - R Version. This is what Robert Hijmans once wrote me when I
Hi Robert,
after my machine crashed I rebooted it. And used to import the ascii
files in the way that you proposed again and now it is working fine! So
I think the very long calculation time must have been caused by my OS.
If I do the import of the ascii files like this:
/
library(raster)
model
Dear All,
thank you very much for your help again. I tried now most of the
different proposals for importing the ascii grids & calculating the
temporal mean. All proposals are more elegant than my script, but
unfortunately the "stacking" and "bricking" is much more time and
computational deman
gt; -MIN: Minimum
>>> Grid (optional output)
>>> -MAX: Maximum
>>> Grid (optional output)
>>> -VAR: Variance
>>> Grid (optional output)
>>> -STDDEV: Standard Deviatio
Dear All,
I like to import a series of asci grids into R. It is a time series of
30 years and I like to calculate the mean monthly sum value for each
grid node for every month of the year. This is way I´m doing this at the
moment.
#read asci files
Month01
EtRR.asci01 <- read.asciigrid("EV
rd with this function. For LOO-CV of
regression kriging (i.e. combined prediction of regression and kriging
method) you would have to write small wrapper functions to be passed
to 'errorest' via the 'model' and 'predict' arguments - see the
Examples section of the
ge.cv(m.glm , loc = boden.ov)
Does anyone have an idea?
Thank you in advance,
Swen
--
------
Dipl. - Geogr. Swen Meyer
Department of Geography
Physical Geography and Environmental Modeling Ludwig-Maximilian University of
Munich
Luisenstrasse 37
80333 Munich/Germany
fon: +49 (0)89 2180-66
)~om.glm, boden.ov, vario.res$var_model, verbose=TRUE). Is
it possible to do this also for the glm part only? Does anyone have an
idea? I can?t find any package function.
Regards,
Swen
--
--
Dipl. - Geogr. Swen Meyer
Department of
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