apca {HDANOVA} | R Documentation |
APCA function for fitting ANOVA Principal Component Analysis models.
apca(formula, data, add_error = TRUE, ...)
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. |
An object of class apca
, inheriting from the general asca
class.
Further arguments and plots can be found in the asca
documentation.
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.
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
data(candies)
ap <- apca(assessment ~ candy, data=candies)
scoreplot(ap)