bootBLB {BiplotML}R Documentation

Fitting a Binary Logistic Biplot using bootstrap methodology

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

bootBLB(
  x,
  k = 2,
  L = 0,
  method = "CG",
  type = 1,
  plot = TRUE,
  sup = TRUE,
  ellipses = FALSE,
  maxit = NULL,
  resamples = 100,
  conf = 0.9,
  col.ind = NULL
)

Arguments

x

Binary matrix.

k

Dimensions number. By default k = 2.

L

Penalization parameter. By default L = 0.

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.

sup

Boolean, if TRUE, rows that are not selected in each resample are treated as supplementary individuals. See details.

ellipses

Draw confidence ellipses. By default is FALSE.

maxit

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

resamples

Number of iterations in the bootstrap process. By default 100.

conf

Level confidence in the ellipses. By default conf=0.90

col.ind

Color for the rows.

Details

Fitting when sup=TRUE ... whereas sup=FALSE ...

Value

Coordenates of the matrix A and B in resamples and Biplot

Author(s)

Giovany Babativa <gbabativam@gmail.com>

References

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.

Milan, L., & Whittaker, J. (1995). Application of the parametric bootstrap to models that incorporate a singular value decomposition. Applied Statistics, 44, 31–49.

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

See Also

plotBLB, performanceBLB

Examples


data("Methylation")
set.seed(02052020)
out.sup <- bootBLB(x = Methylation, ellipses = FALSE)
out <- bootBLB(x = Methylation, sup = FALSE, ellipses = TRUE)


[Package BiplotML version 1.0.1 Index]