apca {HDANOVA}R Documentation

ANOVA Principal Component Analysis - APCA

Description

APCA function for fitting ANOVA Principal Component Analysis models.

Usage

apca(formula, data, add_error = TRUE, ...)

Arguments

formula

Model formula accepting a single response (block) and predictors.

data

The data set to analyse.

add_error

Add error to LS means (default = TRUE).

...

Additional parameters for the asca_fit function.

Value

An object of class apca, inheriting from the general asca class. Further arguments and plots can be found in the asca documentation.

References

Harrington, P.d.B., Vieira, N.E., Espinoza, J., Nien, J.K., Romero, R., and Yergey, A.L. (2005) Analysis of variance–principal component analysis: A soft tool for proteomic discovery. Analytica chimica acta, 544 (1-2), 118–127.

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

Examples

data(candies)
ap <- apca(assessment ~ candy, data=candies)
scoreplot(ap)

[Package HDANOVA version 0.8.1 Index]