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ASCA Result Methods

Source: R/asca_results.R
asca_results.Rd

Standard result computation and extraction functions for ASCA (asca).

Usage

# S3 method for class 'asca'
print(x, ...)

# S3 method for class 'asca'
summary(object, extended = TRUE, df = FALSE, ...)

# S3 method for class 'summary.asca'
print(x, digits = 2, ...)

# S3 method for class 'asca'
loadings(object, factor = 1, ...)

# S3 method for class 'asca'
scores(object, factor = 1, ...)

projections(object, ...)

# S3 method for class 'asca'
projections(object, factor = 1, ...)

Arguments

x

asca object.

...

additional arguments to underlying methods.

object

asca object.

extended

Extended output in summary (default = TRUE).

df

Show degrees of freedom in summary (default = FALSE).

digits

integer number of digits for printing.

factor

integer/character for selecting a model factor.

Value

Returns depend on method used, e.g. projections.asca returns projected samples, scores.asca return scores, while print and summary methods return the object invisibly.

Details

Usage of the functions are shown using generics in the examples in asca. Explained variances are available (block-wise and global) through blockexpl and print.rosaexpl. Object printing and summary are available through: print.asca and summary.asca. Scores and loadings have their own extensions of scores() and loadings() through scores.asca and loadings.asca. Special to ASCA is that scores are on a factor level basis, while back-projected samples have their own function in projections.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

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: asca_fit. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots