Fits a binary lo gistic biplot to a binary data matrix.
BinaryLogisticBiplot(x, dim = 2, compress = FALSE, init = "mca",
method = "EM", rotation = "none", tol = 1e-04,
maxiter = 100, penalization = 0.2, similarity = "Simple_Matching", ...)A Logistic Biplot object.
The binary data matrix
Dimension of the solution
Compress the data before the fitting (not yet implemented)
Type of initial configuration. ("random", "mirt", "PCoA", "mca")
Method to fit the logistic biplot ("EM", "Joint", "mirt", "JointGD", "AlternatedGD", "External", "Recursive")
Rotation of the solution ("none", "oblimin", "quartimin", "oblimax" ,"entropy", "quartimax", "varimax", "simplimax" ) see GPARotation
Tolerance for the algorithm
Maximum number of iterations.
Panalization for the different algorithms
Similarity coefficient for the initial configuration or the external model
Any other argument for each particular method.
Jose Luis Vicente Villardon
Fits a binary lo gistic biplot to a binary data matrix.
Different Initial configurations can be selected:
1.- random : Random coordinates for each point.
2.- mirt: scores of the procedure mirt (Multidimensional Item Response Theory)
3.- PCoA: Principal Coordinates Analysis
4.- mca: Multiple Correspondence Analysis
We can use also different methods for the estimation
1.- Joint: Joint estimation of the row and column parameters. The Initial alorithm.
2.- EM: Marginal Maximum Likelihood
3.- mirt: Similar to the previous but fitted using the package mirt.
4.- JointGD: Joint estimation of the row and column methods using the gradient descent method.
5.- AlternatedGD: Alternated estimation of the row and column methods using the gradient descent method.
6.- External: Logistic fits on the Principal Coordinates Analysis.
7.- Recursive: Recursive (one axis at a time) estimation of the row and column methods using the gradient descent method. This is similar to the NIPALS algorithm for PCA
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.
BinaryLogBiplotJoint, BinaryLogBiplotEM, BinaryLogBiplotGD, BinaryLogBiplotMirt,
# data(spiders)
# X=Dataframe2BinaryMatrix(spiders)
# logbip=BinaryLogBiplotGD(X,penalization=0.1)
# plot(logbip, Mode="a")
# summary(logbip)
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