View source: R/BinaryLogBiplotGDRecursive.R

BinaryLogBiplotGDRecursive | R Documentation |

Binary Logistic Biplot with Recursive Gradient Descent Estimation. An external optimization function is used to calculate the parameters.

```
BinaryLogBiplotGDRecursive(X, freq = matrix(1, nrow(X), 1), dim = 2, tolerance = 1e-04,
penalization = 0.2, num_max_iters = 100,
RotVarimax = FALSE, OptimMethod = "CG",
Initial = "random", ...)
```

`X` |
A binary data matrix |

`freq` |
Frequencies of each row. When adequate. |

`dim` |
Dimension of the final solution. |

`tolerance` |
Tolerance for convergence of the algorithm. |

`penalization` |
Ridge penalization constant. |

`num_max_iters` |
Maximum number of iterations of the algorithm. |

`RotVarimax` |
Should the final solution be rotated. |

`OptimMethod` |
Optimization method used by |

`Initial` |
Initial configuration to start the iterations. |

`...` |
Aditional parameters used by the optimization function. |

Fits a binary logistic biplot using recursive gradient descent. The general function `optim`

is used to optimize the loss function. Conjugate gradien is used as a default although other alternatives can be USED. It can be considered as a generalization of the NIPALS algorithm for a matrix of binary data.

An object of class "Binary.Logistic.Biplot".

JosÃ© Luis Vicente Villardon

Vicente-Villardon, J. L., Galindo, M. P. and Blazquez, A. (2006) Logistic Biplots. In Multiple Correspondence AnÃ¡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.

```
data(spiders)
X=Dataframe2BinaryMatrix(spiders)
logbip=BinaryLogBiplotGDRecursive(X,penalization=0.1)
plot(logbip, Mode="a")
summary(logbip)
```

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

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