s_RFSRC: Random Forest for Classification, Regression, and Survival...
In egenn/rtemis: Machine Learning and Visualization

s_RFSRC

R Documentation

Random Forest for Classification, Regression, and Survival [C, R, S]

Description

Train a Random Forest for Regression, Classification, or Survival Regression using randomForestSRC

Usage

s_RFSRC(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  n.trees = 1000,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  bootstrap = "by.root",
  mtry = NULL,
  importance = TRUE,
  proximity = TRUE,
  nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1,
  nodedepth = NULL,
  na.action = "na.impute",
  trace = FALSE,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

`x`	Numeric vector or matrix of features, i.e. independent variables
`y`	Numeric vector of outcome, i.e. dependent variable
`x.test`	(Optional) Numeric vector or matrix of validation set features must have set of columns as `x`
`y.test`	(Optional) Numeric vector of validation set outcomes
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`n.trees`	Integer: Number of trees to grow. The more the merrier.
`weights`	Numeric vector: Weights for cases. For classification, `weights` takes precedence over `ifw`, therefore set `weights = NULL` if using `ifw`. Note: If `weight` are provided, `ifw` is not used. Leave NULL if setting `ifw = TRUE`.
`ifw`	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If `weights` are provided, `ifw` is not used.
`ifw.type`	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
`upsample`	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness
`downsample`	Logical: If TRUE, downsample majority class to match size of minority class
`resample.seed`	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
`bootstrap`	Character:
`mtry`	Integer: Number of features sampled randomly at each split
`importance`	Logical: If TRUE, calculate variable importance.
`proximity`	Character or Logical: "inbag", "oob", "all", TRUE, or FALSE; passed to `randomForestSRC::rfsrc`
`nodesize`	Integer: Minimum size of terminal nodes.
`nodedepth`	Integer: Maximum tree depth.
`trace`	Integer: Number of seconds between messages to the console.
`print.plot`	Logical: if TRUE, produce plot using `mplot3` Takes precedence over `plot.fitted` and `plot.predicted`.
`plot.fitted`	Logical: if TRUE, plot True (y) vs Fitted
`plot.predicted`	Logical: if TRUE, plot True (y.test) vs Predicted. Requires `x.test` and `y.test`
`plot.theme`	Character: "zero", "dark", "box", "darkbox"
`question`	Character: the question you are attempting to answer with this model, in plain language.
`verbose`	Logical: If TRUE, print summary to screen.
`outdir`	Optional. Path to directory to save output
`save.mod`	Logical: If TRUE, save all output to an RDS file in `outdir` `save.mod` is TRUE by default if an `outdir` is defined. If set to TRUE, and no `outdir` is defined, outdir defaults to `paste0("./s.", mod.name)`
`...`	Additional arguments to be passed to `randomForestSRC::rfsrc`

Details

For Survival Regression, y must be an object of type Surv, created using survival::Surv(time, status) mtry is the only tunable parameter, but it usually only makes a small difference and is often not tuned.

Value

Object of class rtMod

Author(s)

E.D. Gennatas

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Other Tree-based methods: s_AdaBoost(), s_AddTree(), s_BART(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GBM(), s_GLMTree(), s_H2OGBM(), s_H2ORF(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MLRF(), s_RF(), s_Ranger(), s_XGBoost(), s_XRF()