% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EM_GGE.R
\name{BiplotImputfun}
\alias{BiplotImputfun}
\title{Biplot Imputation Method}
\usage{
BiplotImputfun(X, precision = 0.01, max.iter = 1000, n_pc = 2)
}
\arguments{
\item{X}{A data frame or matrix with genotypes in rows and 
environments in columns.}

\item{precision}{(optional) Convergence threshold. The algorithm stops 
when the relative change in imputed values is less than this value. 
Default is 0.01.}

\item{max.iter}{(optional) Maximum number of iterations. Default is 1000.}

\item{n_pc}{Number of principal components to use for imputation. 
Default is 2.}
}
\value{
A list containing:
\itemize{
  \item \code{X_imputed}: The final matrix with missing values filled.
  \item \code{iteration}: Total number of iterations performed.
  \item \code{convergence}: Final relative change reached.
  \item \code{fitted}: The fitted values from the AMMI/Biplot model.
}
}
\description{
This function implements the Biplot imputation method as 
proposed by Yan (2013). It is an iterative algorithm that uses the singular 
value decomposition (SVD) to impute missing values in a genotype by 
environment matrix.
}
\references{
Yan, W. (2013). \emph{Biplot analysis of incomplete two-way data}. 
Crop Science, 53(1), 48-57. \doi{10.2135/cropsci2012.05.0301}

Arciniegas-Alarcón, S., García-Peña, M., Krzanowski, W., & Dias, C. T. S. 
(2014b). \emph{An alternative methodology for imputing missing data in 
trials with genotype-by-environment interaction: some new aspects}. 
Biometrical Letters, 51(2), 75-88. \doi{10.2478/bile-2014-0006}
}
\keyword{internal}
