| rfsrc.fast {randomForestSRC} | R Documentation |
Fast approximate random forests using subsampling with forest options set to encourage computational speed. Applies to all families.
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,
...)
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 |
ntime |
Integer value used for survival to
constrain ensemble calculations to a grid of |
forest |
Should the forest object be returned? |
... |
Further arguments to be passed to |
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.
An object of class (rfsrc, grow).
Hemant Ishwaran and Udaya B. Kogalur
## ------------------------------------------------------------
## 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)