GD.Biplot: Biplot for continuous data based on gradient descent methods

In MultBiplotR: Multivariate Analysis Using Biplots in R

Biplot for continuous data based on gradient descent methods

Description

Biplot for continuous data based on gradient descent methods.

Usage

GD.Biplot(X, dimension = 2, Scaling = 5, 
         lambda = 0.01, OptimMethod = "CG", 
         Orthogonalize = FALSE, Algorithm = "Alternated", 
         sup.rows = NULL, sup.cols = NULL,
         grouping = NULL, tolerance = 1e-04, 
         num_max_iters = 300, Initial = "random")

Arguments

X	A data matrix with continuous variables.
dimension	Dimension of the final solution.
Scaling	Transformation of the raw data matrix before the calculation of the biplot.
lambda	Constant for the ridge Penalization
OptimMethod	Optimization method passed to the optim function. By default is CG (Conjugate Gradient).
Orthogonalize	Should the solution be ortogonalized.
Algorithm	Algorithm to calculate the Biplot. (Alternated, Joint, Recursive)
sup.rows	Supplementary Rows. (not working now)
sup.cols	Supplementary Columns. (not working now)
grouping	Grouping factor for the within groups transformation.
tolerance	Tolerance for convergence
num_max_iters	Maximum number of iterations.
Initial	Initial Configuration

Details

The function calculates a bilot using gradient descent methods. The function optim is used to optimize the loss function. By default CG (Conjugate Gradient) method is used althoug other possibilities can be used.

Value

An object of class "ContinuousBiplot" is returned.

Author(s)

Jose Luis Vicente Villardon

Examples

data("Protein")
X=Protein[,3:11]
gdbip=GD.Biplot(X, dimension=2, Algorithm="Joint", 
Orthogonalize=FALSE, lambda=0.3, Initial="random")
plot(gdbip)
summary(gdbip)

MultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m.