asca_plots: ASCA Plot Methods

In HDANOVA: High-Dimensional Analysis of Variance

ASCA Plot Methods

Description

Various plotting procedures for asca objects.

Usage

## S3 method for class 'asca'
loadingplot(object, factor = 1, comps = 1:2, ...)

## S3 method for class 'asca'
scoreplot(
  object,
  factor = 1,
  comps = 1:2,
  within_level = "all",
  pch.scores = 19,
  pch.projections = 1,
  gr.col = NULL,
  projections = TRUE,
  spider = FALSE,
  ellipsoids,
  confidence,
  xlim,
  ylim,
  xlab,
  ylab,
  legendpos,
  ...
)

permutationplot(object, factor = 1, xlim, xlab = "SSQ", main, ...)

Arguments

object	asca object.
factor	integer/character for selecting a model factor. If factor <= 0 or "global", the PCA of the input is used (negativ factor to include factor level colouring with global PCA).
comps	integer vector of selected components.
...	additional arguments to underlying methods.
within_level	MSCA parameter for chosing plot level (default = "all").
pch.scores	integer plotting symbol.
pch.projections	integer plotting symbol.
gr.col	integer vector of colours for groups.
projections	Include backprojections in score plot (default = TRUE).
spider	Draw lines between group centers and backprojections (default = FALSE).
ellipsoids	character "confidence" or "data" ellipsoids for balanced fixed effect models.
confidence	numeric vector of ellipsoid confidences, default = c(0.4, 0.68, 0.95).
xlim	numeric x limits.
ylim	numeric y limits.
xlab	character x label.
ylab	character y label.
legendpos	character position of legend.
main	Plot title.

Details

Usage of the functions are shown using generics in the examples in asca. Plot routines are available as scoreplot.asca and loadingplot.asca.

Value

The plotting routines have no return.

References

• 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.

See Also

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

HDANOVA documentation built on April 12, 2025, 2:16 a.m.