bootBLB: Fitting a Binary Logistic Biplot using bootstrap methodology

In BiplotML: Biplots Estimation with Algorithms ML

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 <[e-mail]>

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)

BiplotML documentation built on April 23, 2022, 1:06 a.m.