cv_LogBip {BiplotML} | R Documentation |
This function run cross-validation for logistic biplot
cv_LogBip( data, k = 0:5, K = 7, method = "MM", type = NULL, plot = TRUE, maxit = NULL )
data |
Binary matrix. |
k |
Dimensions to analyze. By default |
K |
folds. 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 |
draw the graph. By default |
maxit |
The maximum number of iterations. Defaults to 100 for the gradient methods, and 2000 for MM algorithm. |
Training error and generalization error for a logistic biplot model.
Giovany Babativa <gbabativam@gmail.com>
Bro R and Kjeldahl K and Smilde AK. (2008). Cross-validation of component models: a critical look at current methods. Analytical and bioanalytical chemistry. 390(5):1241-1251
Wold S. (1978). Cross-validatory estimation of the number of components in factor and principal components models. Technometrics. 20(4):397–405.
LogBip, pred_LB, fitted_LB, simBin
set.seed(1234) x <- simBin(n = 100, p = 50, k = 3, D = 0.5, C = 20) # cross-validation with coordinate descendent MM algorithm cv_MM <- cv_LogBip(data = x$X, k=0:5, method = "MM", maxit = 1000) # cross-validation with CG Fletcher-Reeves algorithm cv_CG <- cv_LogBip(data = x$X, k=0:5, method = "CG", type = 1)