BiplotImputfun: Biplot Imputation Method

In geneticae: Statistical Tools for the Analysis of Multi Environment Agronomic Trials

Biplot Imputation Method

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.

Usage

BiplotImputfun(X, precision = 0.01, max.iter = 1000, n_pc = 2)

Arguments

X	A data frame or matrix with genotypes in rows and environments in columns.
precision	(optional) Convergence threshold. The algorithm stops when the relative change in imputed values is less than this value. Default is 0.01.
max.iter	(optional) Maximum number of iterations. Default is 1000.
n_pc	Number of principal components to use for imputation. Default is 2.

Value

A list containing:

• X_imputed: The final matrix with missing values filled.

• iteration: Total number of iterations performed.

• convergence: Final relative change reached.

• fitted: The fitted values from the AMMI/Biplot model.

References

Yan, W. (2013). Biplot analysis of incomplete two-way data. Crop Science, 53(1), 48-57. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2135/cropsci2012.05.0301")}

Arciniegas-Alarcón, S., García-Peña, M., Krzanowski, W., & Dias, C. T. S. (2014b). An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects. Biometrical Letters, 51(2), 75-88. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2478/bile-2014-0006")}

geneticae documentation built on April 17, 2026, 1:07 a.m.