View source: R/BinaryLogBiplotGD.R
BinaryLogBiplotGD | R Documentation |
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", ...)
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 |
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. |
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.
An object of class "Binary.Logistic.Biplot".
Jose Luis Vicente-Villardon
Vicente-Villardon, J. L., Galindo, M. P. and Blazquez, A. (2006) Logistic Biplots. In Multiple Correspondence AnĂ¡lisis 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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