BinaryLogisticBiplot function

Binary Logistic Biplot

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", ...)

Arguments

• x: The binary data matrix
• dim: Dimension of the solution
• compress: Compress the data before the fitting (not yet implemented)
• init: Type of initial configuration. ("random", "mirt", "PCoA", "mca")
• method: Method to fit the logistic biplot ("EM", "Joint", "mirt", "JointGD", "AlternatedGD", "External", "Recursive")
• rotation: Rotation of the solution ("none", "oblimin", "quartimin", "oblimax" ,"entropy", "quartimax", "varimax", "simplimax" ) see GPARotation
• tol: Tolerance for the algorithm
• maxiter: Maximum number of iterations.
• penalization: Panalization for the different algorithms
• similarity: Similarity coefficient for the initial configuration or the external model
• ...: Any other argument for each particular method.

Details

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

Returns

A Logistic Biplot object.

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)

Jose Luis Vicente Villardon

See Also

BinaryLogBiplotJoint, BinaryLogBiplotEM, BinaryLogBiplotGD, BinaryLogBiplotMirt,

Examples

# data(spiders)
# X=Dataframe2BinaryMatrix(spiders)

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