Various plotting procedures for asca objects.
# S3 method for asca
loadingplot(object, factor = 1, comps = 1:2, ...)# S3 method for asca
scoreplot(
object,
factor = 1,
comps = 1:2,
pch.scores = 19,
pch.projections = 1,
gr.col = 1:nlevels(object$effects[[factor]]),
ellipsoids,
confidence,
xlim,
ylim,
xlab,
ylab,
legendpos,
...
)
The plotting routines have no return.
asca object.
integer/character for selecting a model factor.
integer vector of selected components.
additional arguments to underlying methods.
integer plotting symbol.
integer plotting symbol.
integer vector of colours for groups.
character "confidence" or "data" ellipsoids for balanced fixed effect models.
numeric vector of ellipsoid confidences, default = c(0.4, 0.68, 0.95).
numeric x limits.
numeric y limits.
character x label.
character y label.
character position of legend.
Usage of the functions are shown using generics in the examples in asca.
Plot routines are available as
scoreplot.asca and loadingplot.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 computation and extraction of results are found in asca_results.