Cross-validated estimation of the empirical misclassification error for boosting parameter selection.
cv.mada(x, y, balance=FALSE, K=10, nu=0.1, mstop=200, interaction.depth=1,
trace=FALSE, plot.it = TRUE, se = TRUE, ...)object with
empirical risks in each cross-validation at boosting iterations
abscissa values at which CV curve should be computed.
The CV curve at each value of fraction
The standard error of the CV curve
...
a data matrix containing the variables in the model.
vector of multi class responses. y must be an integer vector from 1 to C for C class problem.
logical value. If TRUE, The K parts were roughly balanced, ensuring that the classes were distributed proportionally among each of the K parts.
K-fold cross-validation
a small number (between 0 and 1) defining the step size or shrinkage parameter.
number of boosting iteration.
used in gbm to specify the depth of trees.
if TRUE, iteration results printed out.
a logical value, to plot the cross-validation error if TRUE.
a logical value, to plot with 1 standard deviation curves.
additional arguments.
mada