performanceBLB: Compare Optimization Algorithms for Binary Logistic Biplot...

In BiplotML: Logistic Biplot Estimation Using Machine Learning Algorithms

Compare Optimization Algorithms for Binary Logistic Biplot Estimation

Description

Fits the binary logistic biplot model using multiple optimization algorithms and returns a summary of their computation time, convergence status, and number of function evaluations, facilitating algorithm selection.

Usage

performanceBLB(xi, k = 2, L = 0, method = NULL, maxit = NULL)

Arguments

xi	A binary matrix.
k	Number of dimensions. Default is k = 2.
L	Ridge penalization parameter. Default is L = 0.
method	Algorithm group to compare: 1 (derivative-free), 2 (gradient, default), 3 (quasi-Newton), or 4 (all).
maxit	Maximum number of iterations per algorithm.

Details

The following algorithm groups are available via the method argument:

• 1 — Derivative-free methods: Nelder-Mead, UOBYQA, NEWUOA.

• 2 — Gradient methods (default): CG, Rcgmin.

• 3 — Quasi-Newton methods: BFGS, L-BFGS-B, nlm, nlminb.

• 4 — All of the above.

Value

A data frame with one row per algorithm and columns:

method

Algorithm name.

evaluat

Final value of the objective function.

convergence

Convergence status.

fevals

Number of function evaluations.

time

Elapsed computation time.

Author(s)

Giovany Babativa <[e-mail]>

References

Nash, J. C. (2011). Unifying optimization algorithms to aid software system users: optimx for R. Journal of Statistical Software, 43(9), 1–14.

Nash, J. C. (2014). On best practice optimization methods in R. Journal of Statistical Software, 60(2), 1–14.

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

gradientDesc

Examples

data("Methylation")
set.seed(123456)

# Gradient methods (default)
performanceBLB(xi = Methylation)
performanceBLB(xi = Methylation, maxit = 150)

# Derivative-free methods
performanceBLB(xi = Methylation, method = 1)
performanceBLB(xi = Methylation, method = 1, maxit = 100)

# Quasi-Newton methods
performanceBLB(xi = Methylation, method = 3)
performanceBLB(xi = Methylation, method = 3, maxit = 100)

# All methods
performanceBLB(xi = Methylation, method = 4)

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