% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/performanceBLB.R
\name{performanceBLB}
\alias{performanceBLB}
\title{Compare Optimization Algorithms for Binary Logistic Biplot Estimation}
\usage{
performanceBLB(xi, k = 2, L = 0, method = NULL, maxit = NULL)
}
\arguments{
\item{xi}{A binary matrix.}

\item{k}{Number of dimensions. Default is \code{k = 2}.}

\item{L}{Ridge penalization parameter. Default is \code{L = 0}.}

\item{method}{Algorithm group to compare: \code{1} (derivative-free),
\code{2} (gradient, default), \code{3} (quasi-Newton), or \code{4} (all).}

\item{maxit}{Maximum number of iterations per algorithm.}
}
\value{
A data frame with one row per algorithm and columns:
  \describe{
    \item{\code{method}}{Algorithm name.}
    \item{\code{evaluat}}{Final value of the objective function.}
    \item{\code{convergence}}{Convergence status.}
    \item{\code{fevals}}{Number of function evaluations.}
    \item{\code{time}}{Elapsed computation time.}
  }
}
\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.
}
\details{
The following algorithm groups are available via the \code{method} argument:
\itemize{
  \item \code{1} --- Derivative-free methods: Nelder-Mead, UOBYQA, NEWUOA.
  \item \code{2} --- Gradient methods (default): CG, Rcgmin.
  \item \code{3} --- Quasi-Newton methods: BFGS, L-BFGS-B, nlm, nlminb.
  \item \code{4} --- All of the above.
}
}
\examples{
\donttest{
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)
}
}
\references{
Nash, J. C. (2011). Unifying optimization algorithms to aid software system
users: optimx for R. \emph{Journal of Statistical Software}, \emph{43}(9),
1--14.

Nash, J. C. (2014). On best practice optimization methods in R.
\emph{Journal of Statistical Software}, \emph{60}(2), 1--14.

Vicente-Villardon, J. L., & Galindo, M. P. (2006). Logistic biplots. In
M. Greenacre & J. Blasius (Eds.), \emph{Multiple Correspondence Analysis and
Related Methods} (pp. 503--521). Chapman & Hall.
}
\seealso{
\code{\link{gradientDesc}}
}
\author{
Giovany Babativa <jgbabativam@unal.edu.co>
}
