BinaryLogisticBiplot: Binary Logistic Biplot

In MultBiplotR: Multivariate Analysis Using Biplots in R

Binary Logistic Biplot

Description

Fits a binary lo gistic biplot to a binary data matrix.

Usage

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

Value

A Logistic Biplot object.

Author(s)

Jose Luis Vicente Villardon

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.

See Also

BinaryLogBiplotJoint, BinaryLogBiplotEM, BinaryLogBiplotGD, BinaryLogBiplotMirt,

Examples

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

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

MultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m.