Specification of the number of boosting iterations, step size
and other parameters for boosting algorithms.
Usage
bst_control(mstop = 50, nu = 0.1, twinboost = FALSE, twintype=1,
threshold=c("standard", "adaptive"), f.init = NULL, coefir = NULL,
xselect.init = NULL, center = FALSE, trace = FALSE, numsample = 50, df = 4,
s = NULL, sh = NULL, q = NULL, qh = NULL, fk = NULL, iter = 10, intercept = FALSE)
Arguments
mstop
an integer giving the number of boosting iterations.
nu
a small number (between 0 and 1) defining the step size or shrinkage parameter.
twinboost
a logical value: TRUE for twin boosting.
twintype
for twinboost=TRUE only. For learner="ls", if twintype=1, twin boosting with weights from magnitude of coefficients in the first round of boosting. If twintype=2, weights are correlations between predicted values in the first round of boosting and current predicted values. For learners not componentwise least squares, twintype=2.
threshold
if threshold="adaptive", the estimated function ctrl$fk is updated in every boosting step. Otherwise, no update for ctrl$fk in boosting steps. Only used if in robust loss functions with the difference convex loss.
f.init
the estimate from the first round of twin boosting. Only useful when twinboost=TRUE and learner="sm" or "tree".
coefir
the estimated coefficients from the first round of twin boosting. Only useful when twinboost=TRUE and learner="ls".
xselect.init
the variable selected from the first round of twin boosting. Only useful when twinboost=TRUE.
center
a logical value: TRUE to center covariates with mean.
trace
a logical value for printout of more details of information during
the fitting process.
numsample
number of random sample variable selected in the first round of twin boosting. This is potentially useful in the future implementation.
df
degree of freedom used in smoothing splines.
s,q
truncation parameter s or frequency q of outliers for robust regression and classification. If s is missing but q is available, s may be computed as the 1-q quantile of robust loss values using conventional software.
sh, qh
threshold value or frequency qh of outliers for Huber regressionn family="huber" or family="rhuberDC".
For family="huber", if sh is not provided, sh is then updated adaptively with the median of y-yhat where yhat is the estimated y in the last boosting iteration. For family="rhuberDC", if sh is missing but qh is available, sh may be computed as the 1-qh quantile of robust loss values using conventional software.
fk
used for robust classification. A function estimate used in difference of convex algorithm
iter
number of iteration in difference of convex algorithm
intercept
logical value, if TRUE, estimation of intercept with linear predictor model
Details
Objects to specify parameters of the boosting algorithms
implemented in bst, via the ctrl argument. The default value of s is -1 if family="thinge", -log(3) if family="tbinom", and 4 if family="binomd"
Value
An object of class bst_control, a list. Note fk may be updated for robust boosting.