Description Usage Arguments Value See Also Examples
Crossvalidated estimation of the empirical risk/error for boosting parameter selection.
1 2 3 4 5  cv.bst(x,y,K=10,cost=0.5,family=c("gaussian", "hinge", "hinge2", "binom", "expo",
"poisson", "tgaussianDC", "thingeDC", "tbinomDC", "binomdDC", "texpoDC", "tpoissonDC",
"clossR", "closs", "gloss", "qloss", "lar"), learner = c("ls", "sm", "tree"),
ctrl = bst_control(), type = c("loss", "error"),
plot.it = TRUE, main = NULL, se = TRUE, n.cores=2, ...)

x 
a data frame containing the variables in the model. 
y 
vector of responses. 
K 
Kfold crossvalidation 
cost 
price to pay for false positive, 0 < 
family 

learner 
a character specifying the componentwise base learner to be used:

ctrl 
an object of class 
type 
crossvalidation criteria. For 
plot.it 
a logical value, to plot the estimated loss or error with cross validation if 
main 
title of plot 
se 
a logical value, to plot with standard errors. 
n.cores 
The number of CPU cores to use. The crossvalidation loop will attempt to send different CV folds off to different cores. 
... 
additional arguments. 
object with
residmat 
empirical risks in each crossvalidation at boosting iterations 
mstop 
boosting iteration steps at which CV curve should be computed. 
cv 
The CV curve at each value of mstop 
cv.error 
The standard error of the CV curve 
family 
loss function types 
...
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Not run:
x < matrix(rnorm(100*5),ncol=5)
c < 2*x[,1]
p < exp(c)/(exp(c)+exp(c))
y < rbinom(100,1,p)
y[y != 1] < 1
x < as.data.frame(x)
cv.bst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls", type="loss")
cv.bst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls", type="error")
dat.m < bst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls")
dat.m1 < cv.bst(x, y, ctrl = bst_control(twinboost=TRUE, coefir=coef(dat.m),
xselect.init = dat.m$xselect, mstop=50), family = "hinge", learner="ls")
## End(Not run)

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