ANOVA Principal Component Analysis - APCA

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)
#> Anova Principal Component Analysis fitted using 'lm' (Linear Model) 
#> - SS type II,  coding, restricted model, least squares estimation 
#>            Sum.Sq. Expl.var.(%)
#> candy     32438.46        72.30
#> assessor   1823.59         4.06
#> num         101.17         0.23
#> Residuals  9388.08        20.92
scoreplot(mod, factor=3, gr.col=rgb(eff/max(eff), 1-eff/max(eff),0), pch.scores="x")