apca: ANOVA Principal Component Analysis - APCA

In HDANOVA: High-Dimensional Analysis of Variance

ANOVA Principal Component Analysis - APCA

Description

APCA function for fitting ANOVA Principal Component Analysis models.

Usage

apca(
  formula,
  data,
  add_error = TRUE,
  contrasts = "contr.sum",
  permute = FALSE,
  perm.type = c("approximate", "exact"),
  ...
)

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).
contrasts	Effect coding: "sum" (default = sum-coding), "weighted", "reference", "treatment".
permute	Number of permutations to perform (default = 1000).
perm.type	Type of permutation to perform, either "approximate" or "exact" (default = "approximate").
...	Additional parameters for the hdanova 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: hdanova. 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)

# Numeric effects
candies$num <- eff <- 1:165
mod <- apca(assessment ~ candy + assessor + num, data=candies)
summary(mod)
scoreplot(mod, factor=3, gr.col=rgb(eff/max(eff), 1-eff/max(eff),0), pch.scores="x")

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