aov_pcaSpectra {ChemoSpec}  R Documentation 
ANOVAPCA is a combination of both methods developed by Harrington. The data is partitioned into submatrices corresponding to each experimental factor, which are then subjected to PCA separately after adding the residual error back. If the effect of a factor is large compared to the residual error, separation along the 1st PC in the score plot should be evident. With this method, the significance of a factor can be visually determined (ANOVAPCA is not blind to group membership). ANOVAPCA with only one factor is the same as standard PCA and gives no additional separation.
aov_pcaSpectra(spectra, fac, type = "class", choice = NULL, showNames = TRUE)
spectra 
An object of S3 class 
fac 
A vector of character strings giving the factors to be used in
the analysis. These should be elements of 
type 
Either classical ("cls") or robust ("rob"); Results in either

choice 
The type of scaling to be performed. See

showNames 
Logical. Show the names of the submatrices in the console. 
A list of PCA results, one for each computed submatrix.
Bryan A. Hanson (DePauw University), Matthew J. Keinsley.
Pinto, Bosc, Nocairi, Barros, and Rutledge. "Using ANOVAPCA for Discriminant Analysis: ..." Analytica Chimica Acta 629.12 (2008): 4755.
Harrington, Vieira, Espinoza, Nien, Romero, and Yergey. "Analysis of Variance–Principal Component Analysis: ..." Analytica Chimica Acta 544.12 (2005): 11827.
The output of this function is used in
used in aovPCAscores
and aovPCAloadings
.
Additional documentation at https://bryanhanson.github.io/ChemoSpec/
## Not run: # This example assumes the graphics output is set to ggplot2 (see ?GraphicsOptions). library("ggplot2") data(metMUD2) # Original factor encoding: levels(metMUD2$groups) # Split those original levels into 2 new ones (recode them) new.grps < list(geneBb = c("B", "b"), geneCc = c("C", "c")) mM3 < splitSpectraGroups(metMUD2, new.grps) # run aov_pcaSpectra PCAs < aov_pcaSpectra(mM3, fac = c("geneBb", "geneCc")) p1 < aovPCAscores(mM3, PCAs, submat = 1, ellipse = "cls") p1 < p1 + ggtitle("aovPCA: B vs b") p1 p2 < aovPCAscores(mM3, PCAs, submat = 2) p2 < p2 + ggtitle("aovPCA: C vs c") p2 p3 < aovPCAscores(mM3, PCAs, submat = 3) p3 < p3 + ggtitle("aovPCA: Interaction Term") p3 p4 < aovPCAloadings(spectra = mM3, PCA = PCAs) p4 < p4 + ggtitle("aov_pcaSpectra: Bb Loadings") p4 ## End(Not run)