randomForestSRC (version 3.3.1)

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

Value

An object of class (rfsrc, grow).

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.

Author

Hemant Ishwaran and Udaya B. Kogalur

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.

See Also

rfsrc

Examples

# \donttest{
## ------------------------------------------------------------
## 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)

# }