LogBip {BiplotML} | R Documentation |

This function estimates the vector *μ*, matrix A and matrix B using the optimization algorithm chosen by the user and applies a bootstrap methodology to determine the confidence ellipses.

LogBip( x, k = 2, method = "MM", type = NULL, plot = TRUE, maxit = NULL, endsegm = 0.9, label.ind = FALSE, col.ind = NULL, draw = c("biplot", "ind", "var"), random_start = FALSE, truncated = TRUE, L = 0 )

`x` |
Binary matrix. |

`k` |
Dimensions number. By default |

`method` |
Method to be used to estimate the parameters. By default |

`type` |
For the conjugate-gradients method. Takes value 1 for the Fletcher–Reeves update, 2 for Polak–Ribiere and 3 for Beale–Sorenson. |

`plot` |
Plot the Bootstrap Logistic Biplot. |

`maxit` |
The maximum number of iterations. Defaults to 100 for the gradient methods, and 500 without gradient. |

`endsegm` |
The segment starts at 0.5 and ends at this value. By default |

`label.ind` |
By default the row points are not labelled. |

`col.ind` |
Color for the rows marks. |

`draw` |
The graph to draw ("ind" for the individuals, "var" for the variables and "biplot" for the row and columns coordinates in the same graph) |

`random_start` |
Logical value; whether to randomly inititalize the parameters. If |

`truncated` |
Find the k largest singular values and vectors of a matrix. |

`L` |
Penalization parameter. By default |

The methods that can be used to estimate the parameters of a logistic biplot

- For methods based on the conjugate gradient use method = "CG" and

- type = 1 for the Fletcher Reeves. - type = 2 for Polak Ribiere. - type = 3 for Hestenes Stiefel. - type = 4 for Dai Yuan.

- To use the iterative coordinate descendent MM algorithm then method = "MM".

- To use the BFGS formula, method = "BFGS".

Coordenates of the matrix A and B, threshold for classification rule

Giovany Babativa <gbabativam@gmail.com>

Babativa-Marquez, J.G. and Vicente-Villardon, J.L. (2021). Logistic biplot by conjugate gradient algorithms and iterated SVD. Mathematics 2021.

John C. Nash (2011). Unifying Optimization Algorithms to Aid Software System Users:optimx for R. Journal of Statistical Software. 43(9). 1–14.

John C. Nash (2014). On Best Practice Optimization Methods in R. Journal of Statistical Software. 60(2). 1–14.

Nocedal, J.;Wright, S. (2006). Numerical optimization; Springer Science & Business Media.

Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), *Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots*. Chapman-Hall

data("Methylation") res <- LogBip(x = Methylation, method = "MM", maxit = 1000)

[Package *BiplotML* version 1.0.1 Index]