randomForestSRC: rfsrc.fast – R documentation

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rfsrc.fast

Fast Random Forests

Description

Fast approximate random forests using subsampling with forest options set to encourage computational speed. Applies to all families.

Usage

rfsrc.fast(formula, data,
  ntree = 500,
  nsplit = 10,
  bootstrap = "by.root",
  ensemble = "oob",
  sampsize = function(x){min(x * .632, max(150, x ^ (3/4)))},
  samptype = "swor",
  samp = NULL,
  ntime = 50,
  forest = FALSE,
  ...)

Arguments

`formula`	A symbolic description of the model to be fit. If missing, unsupervised splitting is implemented.
`data`	Data frame containing the y-outcome and x-variables.
`ntree`	Number of trees.
`nsplit`	Non-negative integer value specifying number of random split points used to split a node (deterministic splitting corresponds to the value zero and is much slower).
`bootstrap`	Bootstrap protocol used in growing a tree.
`ensemble`	Specifies the type of ensemble. We request only out-of-sample which corresponds to "oob".
`sampsize`	Function specifying size of subsampled data. Can also be a number.
`samptype`	Type of bootstrap used.
`samp`	Bootstrap specification when `"by.user"` is used.
`ntime`	Integer value used for survival to constrain ensemble calculations to a grid of `ntime` time points.
`forest`	Should the forest object be returned? Turn this on if you want prediction on test data but for big data this can be large.
`...`	Further arguments to be passed to `rfsrc`.

Details

Calls rfsrc under various options (including subsampling) to encourage computational speeds. This will provide a good approximation but will not be as good as default settings of rfsrc.

Value

An object of class (rfsrc, grow).

Author(s)

Hemant Ishwaran and Udaya B. Kogalur

Examples

## ------------------------------------------------------------
## Iowa housing regression example
## ------------------------------------------------------------

## load the Iowa housing data
data(housing, package = "randomForestSRC")

## do quick and *dirty* imputation
housing <- impute(SalePrice ~ ., housing,
         ntree = 50, nimpute = 1, splitrule = "random")

## grow a fast forest
o1 <- rfsrc.fast(SalePrice ~ ., housing)
o2 <- rfsrc.fast(SalePrice ~ ., housing, nodesize = 1)
print(o1)
print(o2)

## grow a fast bivariate forest
o3 <- rfsrc.fast(cbind(SalePrice,Overall.Qual) ~ ., housing)
print(o3)

## ------------------------------------------------------------
## White wine classification example
## ------------------------------------------------------------

data(wine, package = "randomForestSRC")
wine$quality <- factor(wine$quality)
o <- rfsrc.fast(quality ~ ., wine)
print(o)


## ------------------------------------------------------------
## pbc survival example
## ------------------------------------------------------------

data(pbc, package = "randomForestSRC")
o <- rfsrc.fast(Surv(days, status) ~ ., pbc)
print(o)

## ------------------------------------------------------------
## WIHS competing risk example
## ------------------------------------------------------------

data(wihs, package = "randomForestSRC")
o <- rfsrc.fast(Surv(time, status) ~ ., wihs)
print(o)

randomForestSRC

Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

v2.11.0

GPL (>= 3)

Authors

Hemant Ishwaran <hemant.ishwaran@gmail.com>, Udaya B. Kogalur <ubk@kogalur.com>

Initial release

2021-03-30