Gradient boosting for optimizing multiclass loss functions with componentwise linear, smoothing splines, tree models as base learners.
1 2 3 4 5 6 7 8 9 10  mbst(x, y, cost = NULL, family = c("hinge", "hinge2", "thingeDC", "closs", "clossMM"),
ctrl = bst_control(), control.tree=list(fixed.depth=TRUE,
n.term.node=6, maxdepth = 1), learner = c("ls", "sm", "tree"))
## S3 method for class 'mbst'
print(x, ...)
## S3 method for class 'mbst'
predict(object, newdata=NULL, newy=NULL, mstop=NULL,
type=c("response", "class", "loss", "error"), ...)
## S3 method for class 'mbst'
fpartial(object, mstop=NULL, newdata=NULL)

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

ctrl 
an object of class 
control.tree 
control parameters of rpart. 
learner 
a character specifying the componentwise base learner to be used:

type 
in 
object 
class of 
newdata 
new data for prediction with the same number of columns as 
newy 
new response. 
mstop 
boosting iteration for prediction. 
... 
additional arguments. 
A linear or nonlinear classifier is fitted using a boosting algorithm for multiclass responses. This function is different from mhingebst
on how to deal with zerotosume constraint and loss functions. If family="hinge"
, the loss function is the same as in mhingebst
but the boosting algorithm is different. If family="hinge2"
, the loss function is different from family="hinge"
: the response is not recoded as in Wang (2012). In this case, the loss function is
∑{I(y_i \neq j)(f_j+1)_+}.
family="thingeDC"
for robust loss function used in the DCB algorithm.
An object of class mbst
with print
, coef
,
plot
and predict
methods are available for linear models.
For nonlinear models, methods print
and predict
are available.
x, y, cost, family, learner, control.tree, maxdepth 
These are input variables and parameters 
ctrl 
the input 
yhat 
predicted function estimates 
ens 
a list of length 
ml.fit 
the last element of 
ensemble 
a vector of length 
xselect 
selected variables in 
coef 
estimated coefficients in each iteration. Used internally only 
Zhu Wang
Zhu Wang (2011), HingeBoost: ROCBased Boost for Classification and Variable Selection. The International Journal of Biostatistics, 7(1), Article 13.
Zhu Wang (2012), Multiclass HingeBoost: Method and Application to the Classification of Cancer Types Using Gene Expression Data. Methods of Information in Medicine, 51(2), 162–7.
cv.mbst
for crossvalidated stopping iteration. Furthermore see
bst_control
1 2 3 4 5 6 7 8 9 10 11 12 13  x < matrix(rnorm(100*5),ncol=5)
c < quantile(x[,1], prob=c(0.33, 0.67))
y < rep(1, 100)
y[x[,1] > c[1] & x[,1] < c[2] ] < 2
y[x[,1] > c[2]] < 3
x < as.data.frame(x)
dat.m < mbst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls")
predict(dat.m)
dat.m1 < mbst(x, y, ctrl = bst_control(twinboost=TRUE,
f.init=predict(dat.m), xselect.init = dat.m$xselect, mstop=50))
dat.m2 < rmbst(x, y, ctrl = bst_control(mstop=50, s=1, trace=TRUE),
rfamily = "thinge", learner = "ls")
predict(dat.m2)

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