pRDA: [Partial] Redundancy Analysis and Principal Components...

In barebone: Multivariate Methods in Ecology with R: Basic Algorithms

Description

[Partial] Redundancy Analysis via QR and singular value decomposition or Principal Components Analysis via singular value decomposition.

Usage

pRDA(Y, X = NULL, Z = NULL, scale = FALSE)
pRDAalt(Y, X = NULL, Z = NULL, scale = FALSE)

Arguments

Y	Dependent Data Matrix.
X	Model matrix of constraints (can be missing).
Z	Model matrix of constraints (can be missing).
scale	Scale dependent matrix.

Value

later...

Note

Function rda is essentially similar. Function RDA is a simpler basic function that only performs Redundancy Analysis. Function pRDAalt takes explicit residuals of X regressed on Z (similarly as CANOCO for Windows software), whereas pRDA trusts QR decomposition to handle the orthogonality.

Author(s)

Jari Oksanen

See Also

RDA, pCCA, svd, qr, rda.

barebone documentation built on May 2, 2019, 5:17 p.m.