HDANOVA: High-Dimensional Analysis of Variance

In HDANOVA: High-Dimensional Analysis of Variance

High-Dimensional Analysis of Variance

Description

This function provides a high-dimensional analysis of variance (HDANOVA) method which can be used alone or as part of a larger analysis, e.g., ASCA, APCA, LiMM-PCA, MSCA or PC-ANOVA. It can be called directly or through the convenince functions asca, apca, limmpca, msca and pcanova.

Usage

hdanova(
  formula,
  data,
  subset,
  weights,
  na.action,
  family,
  unrestricted = FALSE,
  add_error = FALSE,
  aug_error = "denominator",
  use_ED = FALSE,
  pca.in = FALSE,
  contrasts = "contr.sum",
  coding,
  equal_baseline = FALSE,
  SStype = "II",
  REML = NULL
)

Arguments

formula	Model formula accepting a single response (block) and predictors. See Details for more information.
data	The data set to analyse.
subset	Expression for subsetting the data before modelling.
weights	Optional object weights.
na.action	How to handle NAs (no action implemented).
family	Error distributions and link function for Generalized Linear Models.
unrestricted	Use unrestricted ANOVA decomposition (default = FALSE).
add_error	Add error to LS means, e.g., for APCA.
aug_error	Augment score matrices in backprojection. Default = "denominator" (of F test), "residual" (force error term), nueric value (alpha-value in LiMM-PCA).
use_ED	Use "effective dimensions" for score rescaling in LiMM-PCA.
pca.in	Compress response before ASCA (number of components).
contrasts	Effect coding: "sum" (default = sum-coding), "weighted", "reference", "treatment".
coding	Defunct. Use 'contrasts' instead.
equal_baseline	Experimental: Set to TRUE to let interactions, where a main effect is missing, e.g., a nested model, be handled with the same baseline as a cross effect model. If TRUE the corresponding interactions will be put in quotation marks and included in the model.frame.
SStype	Type of sum-of-squares: "I" = sequential, "II" (default) = last term, obeying marginality, "III" = last term, not obeying marginality.
REML	Parameter to mixlm: NULL (default) = sum-of-squares, TRUE = REML, FALSE = ML.

Value

An hdanova object containing loadings, scores, explained variances, etc. The object has associated plotting (asca_plots) and result (asca_results) functions.

Examples

# Load candies data
data(candies)

# Basic HDANOVA model with two factors
mod <- hdanova(assessment ~ candy + assessor, data=candies)
summary(mod)

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