BooST: Estimate BooST

Description Usage Arguments Value References Examples

View source: R/BooST.R

Description

Estimates Boosting of Smooth Trees (BooST)

Usage

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BooST(
  x,
  y,
  v = 0.2,
  p = 2/3,
  d_max = 4,
  gamma = seq(0.5, 5, 0.01),
  M = 300,
  display = FALSE,
  stochastic = FALSE,
  s_prop = 0.5,
  node_obs = nrow(x)/200,
  random = FALSE
)

Arguments

x

Design matrix with explanatory variables.

y

Response variable.

v

Learning rate (default 0.2).

p

Proportion of variables tested in each node split (default 2/3).

d_max

Number of splits in each tree (default 4).

gamma

Transiction function intensity. Bigger numbers makes the transition less smoth. The default is a sequence of values (0.5:5) to be randomized in each new node. Multiple values may be supplied in a vector to increase the model randomness.

M

Number of trees.

display

If TRUE, displays iteration counter.

stochastic

If TRUE the model will be estimated using Stochasting Gradient Boosting.

s_prop

Used only if stochastic=TRUE. Determines the proportion of data used in each tree.

node_obs

Equivalent to the minimum number of observations in a termina node for a discrete tree.

random

If TRUE trees are grown randomly (default = FALSE)

Value

An object with S3 class "Boost".

Model

A list with all trees.

fitted.values

Final model fitted values.

brmse

Boost rmse in each iteratiob.

Model

A list with all trees.

ybar

Average value of y used in the first iteration.

v

Chosen learning rate.

rho

Vector of gradient estimates for each iteration.

nvar

Numver of variables in x

varnames

colnames of x to be used in other functions.

params

Model parameters.

call

The matched call.

References

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Examples

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gabrielrvsc/BooST documentation built on July 27, 2021, 3:08 p.m.