sdv_MM: Fit a Binary Logistic Biplot via Coordinate Descent MM...

In BiplotML: Logistic Biplot Estimation Using Machine Learning Algorithms

Fit a Binary Logistic Biplot via Coordinate Descent MM Algorithm

Description

Estimates the intercept vector \mu, the row-marker matrix A, and the column-marker matrix B using an iterative coordinate descent Majorization-Minimization (MM) algorithm. This is the low-level function called by LogBip when method = "MM".

Usage

sdv_MM(
  x,
  k = 5,
  iterations = 1000,
  truncated = TRUE,
  random = FALSE,
  epsilon = 1e-04
)

Arguments

x	A binary matrix with no missing values.
k	Number of dimensions. Default is k = 5.
iterations	Maximum number of iterations. Default is 1000.
truncated	Logical; if TRUE (default for large matrices), the truncated SVD from RSpectra is used to speed up computation.
random	Logical; if TRUE, parameters are initialised randomly. Default is FALSE (SVD initialisation).
epsilon	Convergence tolerance. The algorithm stops when the relative decrease in the loss function is below this value. Default is 1e-4.

Value

A named list with components:

mu

Estimated intercept vector of length p.

A

Estimated row-marker matrix (n \times k).

B

Estimated column-marker matrix (p \times k).

iterations

Number of iterations performed.

loss_func

Vector of normalised loss-function values at each iteration.

Author(s)

Giovany Babativa <[e-mail]>

References

Babativa-Marquez, J. G., & Vicente-Villardon, J. L. (2021). Logistic biplot by conjugate gradient algorithms and iterated SVD. Mathematics, 9(16), 2015. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/math9162015")}

Vicente-Villardon, J. L., & Galindo, M. P. (2006). Logistic biplots. In M. Greenacre & J. Blasius (Eds.), Multiple Correspondence Analysis and Related Methods (pp. 503–521). Chapman & Hall.

See Also

LogBip, cv_LogBip

Examples

data("Methylation")
out <- sdv_MM(x = Methylation)

BiplotML documentation built on May 8, 2026, 5:06 p.m.