tepRA: A 'TExPosition'-type version of Redundancy Analysis...

In HerveAbdi/data4PCCAR: Some data sets and R-functions for PCA and CA to accompany Abdi & Beaton (to appear, 2023) Principal Component and Correspondence Analysis with R

A TExPosition-type version of Redundancy Analysis (RA).Temporary Version (14-07-2019).

Description

tepRA: A TExPosition-type version of Redundancy Analysis (RA). Temporary Version. This version will soon be revised to take into account the new GSVD-package from Derek Beaton. Note: This is a temporary version.

Usage

tepRA(
  DATA1,
  DATA2,
  center1 = TRUE,
  scale1 = "SS1",
  center2 = TRUE,
  scale2 = "SS1",
  DESIGN = NULL,
  make_design_nominal = TRUE,
  graphs = TRUE,
  k = 0
)

Arguments

DATA1	an N*I matrix of quantitative data.
DATA2	an N*J matrix of quantitative data.
center1	when TRUE (default) DATA1 will be centered.
scale1	when TRUE (default) DATA1 will be normalized. Depends upon ExPosition function expo.scale whose description is: boolean, text, or (numeric) vector. If boolean or vector, it works just like scale. The following text options are available: 'z': z-score normalization, 'sd': standard deviation normalization, 'rms': root mean square normalization, 'ss1': sum of squares (of columns) equals 1 (i.e., column vector of length of 1).
center2	when TRUE (default) DATA2 will be centered.
scale2	when TRUE (default) DATA2 will be normalized (same options as for scale1).
DESIGN	a design matrix to indicate if the rows comprise several groups.
make_design_nominal	a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.
graphs	a boolean. If TRUE, graphs and plots are provided (via TExPosition::tepGraphs).
k	number of components to return.

Value

See #ExPosition::epGPCA ExPosition::corePCA ad TExPosition for details on what is returned. In addition to the values returned: tepRA returns

lx: the latent variables for DATA1, and ly: the latent variables for DATA2'

data1.norm: the center and scale information for DATA1. and data2.norm: the center and scale information for DATA2.

Author(s)

Vincent Guillemot, Derek Beaton, Hervé Abdi

References

Abdi H., Eslami, A., Guillemot, V., & Beaton D. (2018). Canonical correlation analysis (CCA). In R. Alhajj and J. Rokne (Eds.), Encyclopedia of Social Networks and Mining (2nd Edition). New York: Springer Verlag.

Examples

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HerveAbdi/data4PCCAR documentation built on Sept. 11, 2022, 4:19 p.m.