proj_LogBip: Fit a Binary Logistic Biplot with Missing Data via Block...

In BiplotML: Logistic Biplot Estimation Using Machine Learning Algorithms

Fit a Binary Logistic Biplot with Missing Data via Block Coordinate Descent

Description

Estimates the intercept vector \mu, the row-marker matrix A, and the column-marker matrix B using a data-projection model with a block coordinate descent algorithm. Missing values in the binary matrix are imputed iteratively during model fitting. This function also allows new individuals to be projected as supplementary rows without refitting the model, since the row markers are derived directly from the estimated column markers. This is the low-level function called by LogBip when method = "PDLB".

Usage

proj_LogBip(x, k = 5, max_iters = 1000, random_start = FALSE, epsilon = 1e-05)

Arguments

x	A binary matrix, possibly containing NA values.
k	Number of dimensions. Default is k = 5.
max_iters	Maximum number of iterations. Default is 1000.
random_start	Logical; if TRUE, parameters are initialised randomly. Default is FALSE (SVD initialisation).
epsilon	Convergence tolerance for the relative decrease in the loss function. Default is 1e-5.

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).

x_est

Imputed binary matrix (missing entries replaced by fitted values).

iter

Number of iterations performed.

loss_funct

Vector of normalised loss-function values at each iteration.

Author(s)

Giovany Babativa <[e-mail]>

References

Babativa-Marquez, J. G., & Vicente-Villardon, J. L. (2026). Logistic biplot with missing data. In process.

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")
set.seed(12345)
n <- nrow(Methylation); p <- ncol(Methylation)
miss <- matrix(rbinom(n * p, 1, 0.2), n, p)
miss <- ifelse(miss == 1, NA, miss)
x_miss <- Methylation + miss
out <- proj_LogBip(x = x_miss, k = 2, max_iters = 1000)

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