performanceBLB {BiplotML} | R Documentation |
This function computes the estimates of A and B matrix with severals algorithms.
performanceBLB(xi, k = 2, L = 0, method = NULL, maxit = NULL)
xi |
Binary matrix. |
k |
Dimensions number. By default |
L |
Penalization parameter. By default |
method |
use value 1 for algorithms without gradient, 2 with gradient, 3 quasi-newton methods or 4 for all methods. By default |
maxit |
The maximum number of iterations. Defaults to 100 for the gradient methods, and 500 without gradient. |
This function compare the process time and convergence of different algorithms without gradient, with gradient or quasi-newton method for estimating the parameters in a Binary Logistic Biplot
data frame with method, time of process, convergence and number of evaluations
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.
Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots. Chapman-Hall
data('Methylation') set.seed(123456) ########### Gradient Methods performanceBLB(xi = Methylation) performanceBLB(xi = Methylation, maxit = 150) ########### Without Gradient Methods performanceBLB(xi = Methylation, method = 1) performanceBLB(xi = Methylation, method = 1, maxit = 100) ########### Quasi-Newton Methods performanceBLB(xi = Methylation, method = 3) performanceBLB(xi = Methylation, method = 3, maxit = 100) ########### All methods performanceBLB(x = Methylation, method = 4)