rfsrc.fast: Fast Random Forests

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

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",
  sampsize = function(x){min(x * .632, max(150, x ^ (3/4)))},
  samptype = "swor",
  samp = NULL,
  ntime = 50,
  forest = FALSE,
  save.memory = TRUE,
  ...)

Arguments

formula	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 can be slower).
bootstrap	Bootstrap protocol used in growing a tree.
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	Save key forest values? Turn this on if you want prediction on test data.
save.memory	Save memory? Setting this to FALSE stores terminal node quantities used for prediction on test data. This yields rapid prediction but can be memory intensive for big data, especially competing risks and survival models.
...	Further arguments to be passed to rfsrc.

Details

Calls rfsrc by choosing options (like 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

See Also

rfsrc

Examples

## ------------------------------------------------------------
## regression 
## ------------------------------------------------------------

## 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)

## ------------------------------------------------------------
## classification 
## ------------------------------------------------------------

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

## ------------------------------------------------------------
## grow fast random survival forests without C-calculation
## use brier score to assess model performance
## compare pure random splitting to logrank splitting
## ------------------------------------------------------------

data(peakVO2, package = "randomForestSRC")
f <- as.formula(Surv(ttodead, died)~.)
o1 <- rfsrc.fast(f, peakVO2, perf.type = "none")
o2 <- rfsrc.fast(f, peakVO2, perf.type = "none", splitrule = "random")
bs1 <- get.brier.survival(o1, cens.model = "km")
bs2 <- get.brier.survival(o2, cens.model = "km")
plot(bs2$brier.score, type = "s", col = 2)
lines(bs1$brier.score, type = "s", col = 4)
legend("bottomright", legend = c("random", "logrank"), fill = c(2,4))

## ------------------------------------------------------------
## competing risks
## ------------------------------------------------------------

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

## ------------------------------------------------------------
## class imbalanced data using gmean performance
## ------------------------------------------------------------

data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
f <- as.formula(status ~ .)
o <- rfsrc.fast(f, breast, perf.type = "gmean")
print(o)

## ------------------------------------------------------------
## class imbalanced data using random forests quantile-classifer (RFQ)
## fast=TRUE => rfsrc.fast
## see imbalanced function for further details
## ------------------------------------------------------------

data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
f <- as.formula(status ~ .)
o <- imbalanced(f, breast, fast = TRUE)
print(o)

randomForestSRC documentation built on Sept. 11, 2024, 7:50 p.m.