Nikos Alexandris wrote:
The thing is by multiplying by 0.0001 thing are worse concerning the
*eigenvalues* (the eigenvectors are the same):
# using the MODIS bands as they are
r.info -r mod_b2
min=0
max=5504
# use of i.pca gives
r.info -h pca_mod_b267.1
[...]
Data Description:
generated by i.pca
Comments:
Eigen values, (vectors), and [percent importance]:
PC1 6307563.04 (-0.6353,-0.6485,-0.4192)[98.71%]
PC2 78023.63 (-0.7124, 0.2828, 0.6422)[1.22%]
PC3 4504.60 (-0.2979, 0.7067,-0.6417)[0.07%]
# using MODIS bands multiplied by 0.0001
r.info -r mod_b2_x
min=0
max=0.5504
# using i.pca gives
r.info -h pca.mod_x.1
[...]
Data Description:
generated by i.pca
Comments:
Eigen values, (vectors), and [percent importance]:
PC1 0.06 (-0.6353,-0.6485,-0.4192)[98.71%]
PC2 0.00 (-0.7124, 0.2828, 0.6422)[1.22%]
PC3 0.00 (-0.2979, 0.7067,-0.6417)[0.07%]
OK, I don't have the full discussion on i.pca in my head, so I don't
know how much sense my comments make. The loadings and percentages
explained variance are identical, that's good. The Eigenvalues are not,
it seems they were calculated from unstandardised (raw) values. For
imagery processing, that may be desired, for other applications AFAIK it
is required that input variables variables (here different bands) are
standardised first so they can be combined and principal components
extracted. 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...
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