BooST: Estimate BooST

Description Usage Arguments Value References Examples

View source: R/BooST.R

Description

Estimates Boosting of Smooth Trees (BooST)

Usage

1
2
3
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)

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.

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

blablabla

Examples

1
## == to be made == ##

gabrielrvsc/BooST documentation built on Aug. 12, 2020, 12:50 p.m.