asca_plots: ASCA Result Methods

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

ASCA Result 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,
  pch.scores = 19,
  pch.projections = 1,
  gr.col = 1:nlevels(object$effects[[factor]]),
  ellipsoids,
  confidence,
  xlim,
  ylim,
  xlab,
  ylab,
  legendpos,
  ...
)

Arguments

object	asca object.
factor	integer/character for selecting a model factor.
comps	integer vector of selected components.
...	additional arguments to underlying methods.
pch.scores	integer plotting symbol.
pch.projections	integer plotting symbol.
gr.col	integer vector of colours for groups.
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.

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

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.

multiblock documentation built on Nov. 18, 2023, 5:06 p.m.