# BinaryLogisticBiplot: Binary Logistic Biplot In MultBiplotR: Multivariate Analysis Using Biplots in R

 BinaryLogisticBiplot R Documentation

## 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 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.

`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.