Binary Logistic Biplot with Gradient Descent Estimation. An external optimization function is used to calculate the parameters.
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", ...)An object of class "Binary.Logistic.Biplot".
A binary data matrix
Frequencies of each row. When adequate.
Dimension of the final solution.
Tolerance for convergence of the algorithm.
Ridge penalization constant.
Maximum number of iterations of the algorithm.
Should the final solution be rotated.
Seed for the random numbers. Used for reproductibility.
Optimization method used by optim.
Initial configuration to start the iterations.
Should te solution be orthogonalized?.
Algorithm for esimation: Joint or alternated.
Aditional parameters used by the optimization function.
Jose Luis Vicente-Villardon
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.
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.
data(spiders)
X=Dataframe2BinaryMatrix(spiders)
logbip=BinaryLogBiplotGD(X,penalization=0.1)
plot(logbip, Mode="a")
summary(logbip)
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