BinaryLogBiplotGD: Binary Logistic Biplot with Gradient Descent Estimation

In MultBiplotR: Multivariate Analysis Using Biplots in R

Binary Logistic Biplot with Gradient Descent Estimation

Description

Binary Logistic Biplot with Gradient Descent Estimation. An external optimization function is used to calculate the parameters.

Usage

BinaryLogBiplotGD(X, freq = matrix(1, nrow(X), 1), dim = 2, tolerance =
                   1e-07, penalization = 0.01, num_max_iters = 100,
                   RotVarimax = FALSE, seed = 0, OptimMethod = "CG",
                   Initial = "random", Orthogonalize = FALSE, Algorithm =
                   "Joint", ...)

Arguments

X	A binary data matrix
freq	Frequencies of each row. When adequate.
dim	Dimension of the final solution.
tolerance	Tolerance for convergence of the algorithm.
penalization	Ridge penalization constant.
num_max_iters	Maximum number of iterations of the algorithm.
RotVarimax	Should the final solution be rotated.
seed	Seed for the random numbers. Used for reproductibility.
OptimMethod	Optimization method used by optim.
Initial	Initial configuration to start the iterations.
Orthogonalize	Should te solution be orthogonalized?.
Algorithm	Algorithm for esimation: Joint or alternated.
...	Aditional parameters used by the optimization function.

Details

Fits a binary logistic biplot using gradient descent. The general function optim is used to optimize the loss function. Conjugate gradien is used as a default although other alternatives can be USED.

Value

An object of class "Binary.Logistic.Biplot".

Author(s)

Jose Luis Vicente-Villardon

References

Vicente-Villardon, J. L., Galindo, M. P. and Blazquez, A. (2006) Logistic Biplots. In Multiple Correspondence Análisis And Related Methods. Grenacre, M & Blasius, J, Eds, Chapman and Hall, Boca Raton.

Demey, J., Vicente-Villardon, J. L., Galindo, M.P. AND Zambrano, A. (2008) Identifying Molecular Markers Associated With Classification Of Genotypes Using External Logistic Biplots. Bioinformatics, 24(24): 2832-2838.

Examples

data(spiders)
X=Dataframe2BinaryMatrix(spiders)

logbip=BinaryLogBiplotGD(X,penalization=0.1)
plot(logbip, Mode="a")
summary(logbip)

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