LogBip {BiplotML}R Documentation

Fitting a Binary Logistic Biplot using optimization methods

Description

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.

Usage

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
)

Arguments

x

Binary matrix.

k

Dimensions number. By default k = 2.

method

Method to be used to estimate the parameters. By default method="CG"

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 endsegm = 0.90.

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 FALSE, algorithm will use an SVD as starting value.

truncated

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

L

Penalization parameter. By default L = 0.

Details

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".

Value

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

Author(s)

Giovany Babativa <gbabativam@gmail.com>

References

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

See Also

plotBLB, pred_LB, fitted_LB

Examples


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


[Package BiplotML version 1.0.1 Index]