R: ASCA Result Methods

asca_plots {multiblock}

R Documentation

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.