boostrq {boostrq}

Fitting a boosting regression quantiles model

Description

Component-wise functional gradient boosting algorithm to fit a quantile regression model.

Usage

boostrq(
  formula,
  data,
  mstop = 100,
  nu = NULL,
  tau = 0.5,
  offset = NULL,
  weights = NULL,
  oobweights = NULL,
  risk = "inbag",
  digits = 10,
  exact.fit = FALSE
)

Arguments

formula

a symbolic description of the model to be fit.

data

a data frame (or data.table) containing the variables stated in the formula.

mstop

number of iterations, as integer

nu

learning rate, as numeric

tau

quantile parameter, as numeric

offset

a numeric vector used as offset.

weights

(optional) a numeric vector indicating which weights to used in the fitting process (default: all observations are equally weighted, with 1).

oobweights

an additional vector of out-of-bag weights, which is used for the out-of-bag risk.

risk

string indicating how the empirical risk should be computed for each boosting iteration. inbag leads to risks computed for the learning sample (i.e. observations with non-zero weights), oobag to risks based on the out-of-bag (i.e. observations with non-zero oobagweights).

digits

number of digits the slope parameter different from zero to be considered the best-fitting component, as integer.

exact.fit

logical, if set to TRUE the negative gradients of exact fits are set to 0.

Value

A (generalized) additive quantile regression model is fitted using the boosting regression quantiles algorithm, which is a functional component-wise boosting algorithm. The base-learner can be specified via the formula object. brq (linear quantile regression) and brqss(nonlinear quantile regression) are available base-learner.

Examples

boosted.rq <-
boostrq(
 formula = mpg ~ brq(cyl * hp) + brq(am + wt),
 data = mtcars,
 mstop = 200,
 nu = 0.1,
 tau = 0.5
)

boosted.rq$mstop()

boosted.rq$selection.freqs()

boosted.rq$coef()

boosted.rq$risk()