Standard result computation and extraction functions for ASCA (asca).
# S3 method for asca
print(x, ...)# S3 method for asca
summary(object, ...)
# S3 method for summary.asca
print(x, digits = 2, ...)
# S3 method for asca
loadings(object, factor = 1, ...)
# S3 method for asca
scores(object, factor = 1, ...)
projections(object, ...)
# S3 method for asca
projections(object, factor = 1, ...)
Returns depend on method used, e.g. projections.asca returns projected samples,
scores.asca return scores, while print and summary methods return the object invisibly.
asca object.
additional arguments to underlying methods.
asca object.
integer number of digits for printing.
integer/character for selecting a model factor.
Usage of the functions are shown using generics in the examples in asca.
Explained variances are available (block-wise and global) through blockexpl and print.rosaexpl.
Object printing and summary are available through:
print.asca and summary.asca.
Scores and loadings have their own extensions of scores() and loadings() through
scores.asca and loadings.asca. Special to ASCA is that scores are on a
factor level basis, while back-projected samples have their own function in projections.asca.
Smilde, A., Jansen, J., Hoefsloot, H., Lamers,R., Van Der Greef, J., and Timmerman, M.(2005). ANOVA-Simultaneous Component Analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.
Liland, K.H., Smilde, A., Marini, F., and Næs,T. (2018). Confidence ellipsoids for ASCA models based on multivariate regression theory. Journal of Chemometrics, 32(e2990), 1–13.
Martin, M. and Govaerts, B. (2020). LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data. Journal of Chemometrics, 34(6), e3232.
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
Common functions for plotting are found in asca_plots.