LogBip | R Documentation |
This function estimates the vector μ, matrix A and matrix B using the optimization algorithm chosen by the user. The PDLB method allows to enter a binary matrix with missing data
LogBip( x, k = 2, method = "MM", type = NULL, plot = TRUE, maxit = NULL, endsegm = 0.9, label.ind = FALSE, col.ind = NULL, draw = c("biplot", "ind", "var"), random_start = FALSE, L = 0, cv_LogBip = FALSE )
x |
Binary matrix. |
k |
Dimensions number. By default |
method |
Method to be used to estimate the parameters. By default |
type |
For the conjugate-gradients method. Takes value 1 for the Fletcher–Reeves update, 2 for Polak–Ribiere and 3 for Beale–Sorenson. |
plot |
Plot the Bootstrap Logistic Biplot. |
maxit |
The maximum number of iterations. Defaults to 100 for the gradient methods, and 500 without gradient. |
endsegm |
The segment starts at 0.5 and ends at this value. By default |
label.ind |
By default the row points are not labelled. |
col.ind |
Color for the rows marks. |
draw |
The graph to draw ("ind" for the individuals, "var" for the variables and "biplot" for the row and columns coordinates in the same graph) |
random_start |
Logical value; whether to randomly inititalize the parameters. If |
L |
Penalization parameter. By default |
cv_LogBip |
Indicates if the procedure is being used for cross validation. |
The methods that can be used to estimate the parameters of a logistic biplot
- For methods based on the conjugate gradient use method = "CG" and
type = 1 for the Fletcher Reeves; type = 2 for Polak Ribiere; type = 3 for Hestenes Stiefel and type = 4 for Dai Yuan.
- To use the iterative coordinate descendent MM algorithm then method = "MM".
- If the binary matrix X has missing data, use method = "PDLB". In case it's required to estimate the row coordinates of other individuals, this method is also the most appropriate. For more details see the paper "Logistic biplot with missing data".
- To use the BFGS formula, method = "BFGS".
Coordenates of the matrix A and B, threshold for classification rule. Furthemore, for the PDLB method, the imputed matrix is returned.
Giovany Babativa <gbabativam@gmail.com>
Babativa-Marquez, J. G., & Vicente-Villardon, J. L. (2022). Logistic biplot with missing data. In Process.
Babativa-Marquez, J. G., & Vicente-Villardon, J. L. (2021). Logistic Biplot by Conjugate Gradient Algorithms and Iterated SVD. Mathematics, 9(16).
John C. Nash (2011). Unifying Optimization Algorithms to Aid Software System Users:optimx for R. Journal of Statistical Software. 43(9). 1–14.
John C. Nash (2014). On Best Practice Optimization Methods in R. Journal of Statistical Software. 60(2). 1–14.
Nocedal, J.;Wright, S. (2006). Numerical optimization; Springer Science & Business Media.
Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots. Chapman-Hall
plotBLB, pred_LB, fitted_LB
data("Methylation") # If the binary matrix has no missing data and does not require the projection # of supplementary individuals, you can use an coordinate descendent MM algorithm res_MM <- LogBip(x = Methylation, method = "MM", maxit = 1000) # If the binary matrix has missing data or requires the projection of supplementary #individuals, use a method based on data projection with a block coordinate descent algorithm 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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