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ASCA Result Methods

Source: R/asca_plots.R
asca_plots.Rd

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.

Value

The plotting routines have no return.

Details

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

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.