Hi
everyone,
I
was looking through the literature on how to handle PC analysis data and saw
that there are several different methods you can use for determining how many
components there are in the series of scans. Included in SixPack are the indicator
function, scree test, and the ability to quickly do the reduced eigenvalue
ratios. I’ve been digging through the literature as to how to calculate
the F-values. The closest to an answer that I got was:
“The above-mentioned reduction of the
body of experimental data, that is, the decision of what components correspond
to the noise and what are the principal components, is now made on the basis of
an F test of the variance associated with eigenvalue k and the summed variance
associated with noise eigenvalues (k+1, ..., c). The null hypothesis is that a
given factor k is a member of the pool of noise factors. The
probability that an F value would be higher than the current
value is given by %SL (percentage of significance level). Thus, the kth factor
is accepted as a principal component if %SL is lower than some test level.”
(Garcia 1995).
We ran the PCA on the reduction of iron
while scanning at increased temperatures. I checked the foil standard but did
not see any shift in the max at 7112, we scanned at 0.5 eV intervals (2 eV
resolution at the beam). I thought I understood what that statement was saying
but I’m almost certain I’m doing something wrong. I have attached
the .xlsx file that I was working on and hope someone can point me to the right
direction. The file includes the components of the PCA and some of the
variances that I was calculating. If there is a paper that someone shows an
actual calculation of this in the supplemental materials that would have been
exactly what I was looking for!
Thanks for the help!
Andrew Campos
Fernandez-Garcia,
M., C. Marquez Alvarez, and G.L. Haller, The Journal of Physical Chemistry,
1995, 99(33), 12565-12569.