Edzer Pebesma wrote:
Markus Metz wrote:
I'm more familiar with non-spatial PCA, so it's high time I read the
manual of i.pca, and the new wiki page on it...
I think there's no such thing as spatial or non-spatial PCA. There's
just PCA.
That was a feeble attempt to buy time to go through some statistics
literature ;-)
So it seems that this thread is about the different values for
eigenvalues. AFAIKT, the answer is in the very first post of this thread
[1]. It seems that i.pca output is supposed to be identical to
prcomp(center=FALSE, scale=FALSE) output in R, because a PCA is
scale-sensitive and the eigenvalue as reported by i.pca is the variance
of the raw, unstandardised data. If outputs are not identical, either R
or grass do some hidden modification or there is a bug in either grass
or R (all within limits, e.g. identical up to the 5th digit in
scientific format is fine?).
Some textbooks give a rule of thumb for further analysis to use only
components with an eigenvalue >=1 which obviously only works if the
eigenvalue is calculated from standardised values (center=TRUE,
scale=TRUE or e.g. r.mapcalc standardised_map = (map - mean) / stddev).
E.g., comparing the results of MODIS raw and MODIS scaled with 0.0001
should give <eigenvalue #x of MODIS scaled> = 1E-8 * <eigenvalue #x of
MODIS raw>.
BTW, the rescaling method of i.pca is not very convincing, as pointed
out by Augustin Lobo. IMHO, fool-proof would be normalization (x - mean)
/ stddev.
[1] http://lists.osgeo.org/pipermail/grass-user/2009-March/049306.html
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