Standard result computation and extraction functions for ASCA (pcanova).
# S3 method for pcanova
summary(object, ...)# S3 method for summary.pcanova
print(x, digits = 2, ...)
# S3 method for pcanova
print(x, ...)
# S3 method for pcanova
summary(object, ...)
Returns depend on method used, e.g. projections.pcanova returns projected samples,
scores.pcanova return scores, while print and summary methods return the object invisibly.
pcanova object.
additional arguments to underlying methods.
pcanova object.
integer number of digits for printing.
Usage of the functions are shown using generics in the examples in pcanova.
Explained variances are available (block-wise and global) through blockexpl and print.rosaexpl.
Object printing and summary are available through:
print.pcanova and summary.pcanova.
Scores and loadings have their own extensions of scores() and loadings() through
scores.pcanova and loadings.pcanova. Special to ASCA is that scores are on a
factor level basis, while back-projected samples have their own function in projections.pcanova.
Luciano G, Næs T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Qual Prefer. 2009;20(3):167-175.
Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova.
Workhorse function underpinning most methods: hdanova.
Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots