proj_LogBip: Fitting a Binary Logistic Biplot with Missing Data Using Data...

In jgbabativam/BiplotML: Biplots Estimation with Algorithms ML

Fitting a Binary Logistic Biplot with Missing Data Using Data Projection and a Block Coordinate Descending Algorithm

Description

This function impute the missing values of a binary dataset X, and estimates the vector μ, matrix A and matrix B using data projection model with a block coordinate descending algorithm.

Usage

proj_LogBip(x, k = 2, max_iters = 1000, random_start = FALSE, epsilon = 1e-05)

Arguments

x	binary matrix.
k	dimensions number. By default k = 2.
max_iters	maximum iterations.
random_start	random initialization
epsilon	convergence criteria

Value

Imputed X matrix and coordenates of the matrix A and B, and μ

Author(s)

Giovany Babativa <[e-mail]>

References

Babativa-Marquez, J. G., & Vicente-Villardon, J. L. (2022). Logistic biplot with missing data. Babativa-Marquez, J. G., & Vicente-Villardon, J. L. (2021). Logistic Biplot by Conjugate Gradient Algorithms and Iterated SVD. Mathematics, 9(16). Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots. Chapman-Hall

See Also

cv_LogBip

Examples

data("Methylation")
set.seed(12345)
n <- nrow(Methylation)
p <- ncol(Methylation)
miss <- matrix(rbinom(n*p, 1, 0.2), n, p) #I simulate some missing data
miss <- ifelse(miss == 1, NA, miss)
x <- Methylation + miss  #Matrix containing missing data
out <- LogBip(x, method = "PDLB", maxit = 1000)

jgbabativam/BiplotML documentation built on July 31, 2022, 11:10 a.m.