[GRASS-user] Calculating eigen
valuesand%varianceexplainedafter PCA analysis
wroberts at csir.co.za
Tue Mar 3 02:27:36 EST 2009
>>> Nikos Alexandris <nikos.alexandris at felis.uni-freiburg.de> 03/03/09
9:04 AM >>>
> I got the svn grass6_devel code from the location you suggested and
> now installed but with lots of errors. Here is the output from the
> 'make' command (see below the lapack.h error). I understand that I
> to cd into the directory where the error occurred and run make inside
> that directory. I do this for the gmath directory and I once again
> receive an error message related to the lapack.h file. The same error
> occurs when running make in other error locations.
> error: expected declaration specifiers or ‘...’ before ‘ftnlen’
> make: *** [OBJ.i686-pc-linux-gnu/del2g.o] Error 1
what are the exact steps you are executing in order to compile
(assuming you work under linux) If you already had done a compilation
before, did you "make distclean" before re-compiling?
(or "sudo make distclean").
I actually figured out the problem about 10 min after I sent you and the
list the mail. My apologies (that actually happens fairly often), I
found the following entry while searching the mail archives of this
My error was related to the lapack.h file which is not used in Grass so
I removed '--with-blas --with-lapack' from my ./configure and hey
presto, everything works fine in the development version, except nviz,
which I am not too concerned about.
> I dont have much more time to spend on this so would ideally just like
> to get the i.pca working in development installation so that I can use
> i.pca and get the variance and eigen values. In my tests run so far I
> have seen that i.pca produces more realistic results, based on
> qualitative assessment. As such, I will use i.pca instead of the
If you don't mind to share some more info about the way you assess the
quality of the components (just even mentioning some will be great - I
am really interested in it).
To be honest I just assess the resulting images visually and see if for
eg. PC1 looks to contain information from both the lidar and aerial
photography. While assessing the results from the 'by-hand' method I
noticed that my lidar data did not really contribute to the first PC
component. Given that both data sets were largely made up of canopy
information I was hoping that PC would contain more canopy info from the
lidar data. Assessing the result of a PCA is fairly difficult I think.
In past research I ran a time series analysis on 20years of NDVI data
and used PCA to analyse trends. Correlation between PC component 1 and
the time series data set generally returned seasonal variations in the
data set. In the present analysis I guess you could do the same although
I am not really sure how useful that would be. For me I would just like
to make sure that I am running the analysis correctly and that the
resulting fusion of the aerial photographs and lidar canopy models
contributes to an enhanced ability to count tree canopies.
When I went back to tests run using the original i.pca I saw that the
first PC seemed to contain more information from both data sets. I am
now running a test using the new i.pca (grass_devel6) with all input
data scaled to 8 bit grey scale and centered using the global mean of
each band. Combined with the changes made by Hamish, I think this final
test will prove very successful.
> Thanks for all your help wrt running pca in grass.
Thanks to the community and Hamish for his time.
Yes, indeed, thanks to Hamish and the rest of the grass-user community
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