bootBLB {BiplotML} | R Documentation |
This function estimates the vector μ, matrix A and matrix B using the optimization algorithm chosen by the user and applies a bootstrap methodology to determine the confidence ellipses.
bootBLB( x, k = 2, L = 0, method = "CG", type = 1, plot = TRUE, sup = TRUE, ellipses = FALSE, maxit = NULL, resamples = 100, conf = 0.9, col.ind = NULL )
x |
Binary matrix. |
k |
Dimensions number. By default |
L |
Penalization parameter. 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. |
sup |
Boolean, if TRUE, rows that are not selected in each resample are treated as supplementary individuals. See details. |
ellipses |
Draw confidence ellipses. By default is FALSE. |
maxit |
The maximum number of iterations. Defaults to 100 for the gradient methods, and 500 without gradient. |
resamples |
Number of iterations in the bootstrap process. By default |
conf |
Level confidence in the ellipses. By default |
col.ind |
Color for the rows. |
Fitting when sup=TRUE ... whereas sup=FALSE ...
Coordenates of the matrix A and B in resamples and Biplot
Giovany Babativa <gbabativam@gmail.com>
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.
Milan, L., & Whittaker, J. (1995). Application of the parametric bootstrap to models that incorporate a singular value decomposition. Applied Statistics, 44, 31–49.
Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots. Chapman-Hall
data("Methylation") set.seed(02052020) out.sup <- bootBLB(x = Methylation, ellipses = FALSE) out <- bootBLB(x = Methylation, sup = FALSE, ellipses = TRUE)