cv.mhingeova {bst}  R Documentation 
Crossvalidated estimation of the empirical misclassification error for boosting parameter selection.
cv.mhingeova(x, y, balance=FALSE, K=10, cost = NULL, nu=0.1, learner=c("tree", "ls", "sm"), maxdepth=1, m1=200, twinboost = FALSE, m2=200, trace=FALSE, plot.it = TRUE, se = TRUE, ...)
x 
a data frame containing the variables in the model. 
y 
vector of multi class responses. 
balance 
logical value. If TRUE, The K parts were roughly balanced, ensuring that the classes were distributed proportionally among each of the K parts. 
K 
Kfold crossvalidation 
cost 
price to pay for false positive, 0 < 
nu 
a small number (between 0 and 1) defining the step size or shrinkage parameter. 
learner 
a character specifying the componentwise base learner to be used:

maxdepth 
tree depth used in 
m1 
number of boosting iteration 
twinboost 
logical: twin boosting? 
m2 
number of twin boosting iteration 
trace 
if TRUE, iteration results printed out 
plot.it 
a logical value, to plot the estimated risks if 
se 
a logical value, to plot with standard errors. 
... 
additional arguments. 
object with
residmat 
empirical risks in each crossvalidation at boosting iterations 
fraction 
abscissa values at which CV curve should be computed. 
cv 
The CV curve at each value of fraction 
cv.error 
The standard error of the CV curve 
...
The functions for balanced cross validation were from R package pmar.