BinaryLogBiplotGDRecursive function

Binary Logistic Biplot with Recursive Gradient Descent Estimation

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

BinaryLogBiplotGDRecursive(X, freq = matrix(1, nrow(X), 1), dim = 2, tolerance = 1e-04, 
                          penalization = 0.2, num_max_iters = 100, 
                          RotVarimax = FALSE, OptimMethod = "CG", 
                          Initial = "random", ...)

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.
• OptimMethod: Optimization method used by optim.
• Initial: Initial configuration to start the iterations.
• ...: Aditional parameters used by the optimization function.

Details

Fits a binary logistic biplot using recursive 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. It can be considered as a generalization of the NIPALS algorithm for a matrix of binary data.

Returns

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

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.

Author(s)

José Luis Vicente Villardon

Examples

data(spiders)
X=Dataframe2BinaryMatrix(spiders)
logbip=BinaryLogBiplotGDRecursive(X,penalization=0.1)
plot(logbip, Mode="a")
summary(logbip)