pcanova_results: PC-ANOVA Result Methods

In HDANOVA: High-Dimensional Analysis of Variance

PC-ANOVA Result Methods

Description

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

Usage

## S3 method for class 'pcanova'
summary(object, ...)

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

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

## S3 method for class 'pcanova'
summary(object, ...)

Arguments

object	pcanova object.
...	additional arguments to underlying methods.
x	pcanova object.
digits	integer number of digits for printing.

Details

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

Value

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

References

Luciano G, Næs T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Qual Prefer. 2009;20(3):167-175.

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.