View source: R/BinaryLogisticBiplot.R

BinaryLogisticBiplot | R Documentation |

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", ...)
```

`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. |

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

A Logistic Biplot object.

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.

`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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