Usage
cv.mhinge(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, ...)Arguments
x
a data frame containing the variables in the model.
y
vector of multi class responses. y must be an interger vector from 1 to C for C class problem.
balance
logical value. If TRUE, The K
parts were roughly balanced, ensuring that the classes were distributed
proportionally among each of the K parts.
cost
price to pay for false positive, 0 < cost < 1; price of false negative is 1-cost.
nu
a small number (between 0 and 1) defining the step size or shrinkage parameter.
learner
a character specifying the component-wise base learner to be used:
ls linear models,
sm smoothing splines,
tree regression trees.
maxdepth
tree depth used in learner=tree
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 TRUE.
se
a logical value, to plot with standard errors.