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,
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 |
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")