proj_LogBip | R Documentation |
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.
proj_LogBip(x, k = 2, max_iters = 1000, random_start = FALSE, epsilon = 1e-05)
x |
binary matrix. |
k |
dimensions number. By default |
max_iters |
maximum iterations. |
random_start |
random initialization |
epsilon |
convergence criteria |
Imputed X matrix and coordenates of the matrix A and B, and μ
Giovany Babativa <gbabativam@gmail.com>
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
cv_LogBip
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)
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