HDANOVA (version 0.8.1)

apca: ANOVA Principal Component Analysis - APCA

Description

APCA function for fitting ANOVA Principal Component Analysis models.

Usage

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

Value

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

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.

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)