R: Fast Random Forests

rfsrc.fast {randomForestSRC} | R Documentation

## 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

### See Also

`rfsrc`

### 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)

[Package randomForestSRC version 2.11.0 Index]