Hi Roger,
I have been following the directions in the lastest GRASS newsletter in
regards to moving GRASS6 data to and from R.
e.g. :
library(spgrass6)
gmeta6 <- gmeta6()
x <- readFLOAT6sp(c("er9","crosc","longc","slope_n","profc","minic","maxic"))
I have been attempting to reproduce your terrain classification example in a
paper that you wrote some time ago [1]. I am having a difficult time
understanding how the results from the clara() clustering results can be
applied converted back into a raster and sent to GRASS.
here is what I have done so far:
#make a matrix of "observations"
morph <- data.frame(cbind(x$er9, x$crosc, x$longc, x$slope_n, x$profc,
x$minic, x$maxic))
#assign names
names(morph) <- c("er9","crosc","longc","slope_n","profc","minic","maxic")
#perform the clustering
require(cluster)
morph.clara <- clara(morph, k=5, stand=T)
#print a summary
print(data.frame(morph.clara$clusinfo))
size max_diss av_diss isolation
1 121180 9.087186 2.110414 2.964906
2 69129 6.820200 2.041174 2.835463
3 84236 13.193372 2.561544 3.556510
4 53631 16.692732 2.725165 6.939917
5 70032 10.193958 1.827320 4.085314
#combine clustered results into a matrix and send to GRASS:
#original raster was 544 rows by 732 cols:
morph.image <- matrix(morph.clara$clustering, nrow=544, ncol=732, byrow=T)
#look at the results:
image(morph.image)
#... it is rotated 90deg CCW !
(see attached)
In your paper [1] you mention that GRASS and R make different assumptions
about the indexing of a matrix... however, I cannot quite figure out from
your example how to rotate this matrix... or if I am even doing this
correctly.
Any thoughts would be greatly appreciated.
Thanks,
[1] Roger Bivand. Integrating GRASS 5.0 and R: GIS and modern statistics for
data analysis. Internal Paper (?)
--
Dylan Beaudette
Soils and Biogeochemistry Graduate Group
University of California at Davis
530.754.7341
(attachments)
