pcanova: Principal Components Analysis of Variance Simultaneous...

In HDANOVA: High-Dimensional Analysis of Variance

Principal Components Analysis of Variance Simultaneous Component Analysis - PC-ANOVA

Description

This is a quite general and flexible implementation of PC-ANOVA.

Usage

pcanova(formula, data, ncomp = 0.9, contrasts = "contr.sum", ...)

Arguments

formula	Model formula accepting a single response (block) and predictor names separated by + signs.
data	The data set to analyse.
ncomp	The number of components to retain, proportion of variation or default = minimum cross-validation error.
contrasts	Effect coding: "sum" (default = sum-coding), "weighted", "reference", "treatment".
...	Additional parameters for the hdanova function.

Details

PC-ANOVA works in the opposite order of ASCA. First the response matrix is decomposed using ANOVA. Then the components are analysed using ANOVA with respect to a design or grouping in the data. The latter can be ordinary fixed effects modelling or mixed models.

Value

A pcanova object containing loadings, scores, explained variances, etc. The object has associated plotting (pcanova_plots) and result (pcanova_results) functions.

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

Examples

# Load candies data
data(candies)

# Basic PC-ANOVA model with two factors, cross-validated opt. of #components
mod <- pcanova(assessment ~ candy + assessor, data = candies)
print(mod)

# PC-ANOVA model with interaction, minimum 90% explained variance
mod <- pcanova(assessment ~ candy * assessor, data = candies, ncomp = 0.9)
print(mod)
summary(mod)

# Tukey group letters for 'candy' per component
lapply(mod$models, function(x)
       mixlm::cld(mixlm::simple.glht(x,
                                     effect = "candy")))

# Result plotting
loadingplot(mod, scatter=TRUE, labels="names")
scoreplot(mod)

# Mixed Model PC-ANOVA, random assessor
mod.mix <- pcanova(assessment ~ candy + r(assessor), data=candies, ncomp = 0.9)
scoreplot(mod.mix)
# Fixed effects
summary(mod.mix)

HDANOVA documentation built on April 12, 2025, 2:16 a.m.