s.CART: Classification and Regression Trees [C, R, S]

Description

Train a CART for regression or classification using rpart

Usage

s.CART(x, y = NULL, x.test = NULL, y.test = NULL, x.name = NULL,
  y.name = NULL, weights = NULL, ipw = TRUE, ipw.type = 2,
  upsample = FALSE, upsample.seed = NULL, method = "auto",
  parms = NULL, minsplit = 2, minbucket = round(minsplit/3),
  cp = 0.01, maxdepth = 20, maxcompete = 0, maxsurrogate = 0,
  usesurrogate = 2, surrogatestyle = 0, xval = 0, cost = NULL,
  model = TRUE, prune.cp = NULL, use.prune.rpart.rt = TRUE,
  return.unpruned = FALSE,
  grid.resample.rtset = rtset.resample("kfold", 5),
  grid.search.type = c("exhaustive", "randomized"),
  grid.randomized.p = 0.1, metric = NULL, maximize = NULL,
  na.action = na.exclude, n.cores = rtCores, print.plot = TRUE,
  plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  verbose = TRUE, grid.verbose = TRUE, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), rtModLog = NULL)

Arguments

Numeric vector or matrix / data frame of features i.e. independent variables

Numeric vector of outcome, i.e. dependent variable

x.test

Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in x

y.test

Numeric vector of testing set outcome

x.name

Character: Name for feature set

y.name

Character: Name for outcome

weights

Numeric vector: Weights for cases. For classification, weights takes precedence over ipw, therefore set weights = NULL if using ipw. Note: If weight are provided, ipw is not used. Leave NULL if setting ipw = TRUE. Default = NULL

ipw

Logical: If TRUE, apply inverse probability weighting (for Classification only). Note: If weights are provided, ipw is not used. Default = TRUE

ipw.type

Integer 0, 1, 2 1: class.weights as in 0, divided by max(class.weights) 2: class.weights as in 0, divided by min(class.weights) Default = 2

upsample

Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Caution: 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

upsample.seed

Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)

method

String: "auto", "anova", "poisson", "class" or "exp". Default = "auto"

parms

List of additional parameters for the splitting function. See rpart::rpart("parms")

minsplit

[gS] Integer: Minimum number of cases that must belong in a node before considering a split. Default = 2

minbucket

[gS] Integer: Minimum number of cases allowed in a child node. Default = round(minsplit/3)

[gS] Float: Complexity threshold for allowing a split. Default = .01

maxdepth

[gS] Integer: Maximum depth of tree. Default = 20

cost

Vector, Float (> 0): One for each variable in the model. See rpart::rpart("cost")

model

Logical: If TRUE, keep a copy of the model. Default = TRUE

prune.cp

[gS] Float: Complexity for cost-complexity pruning after tree is built

use.prune.rpart.rt

[Testing only, do not change]

return.unpruned

Logical: If TRUE and prune.cp is set, return unpruned tree under extra in rtMod

grid.resample.rtset

List: Output of rtset.resample defining gridSearchLearn parameters. Default = rtset.resample("kfold", 5)

grid.search.type

String: Type of grid search to perform: "exhaustive" or "randomized". Default = "exhaustive"

grid.randomized.p

Float (0, 1): If grid.search.type = "randomized", randomly run this proportion of combinations. Default = .1

metric

String: Metric to minimize, or maximize if maximize = TRUE during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.

maximize

Logical: If TRUE, metric will be maximized if grid search is run. Default = FALSE

na.action

How to handle missing values. See ?na.fail

n.cores

Integer: Number of cores to use. Defaults to available cores reported by future::availableCores(), unles option rt.cores is set at the time the library is loaded

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

String: "zero", "dark", "box", "darkbox"

question

String: the question you are attempting to answer with this model, in plain language.

verbose

Logical: If TRUE, print summary to screen.

grid.verbose

Logical: Passed to gridSearchLearn

outdir

Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE

save.mod

Logical. If TRUE, save all output as 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)

Value

Object of class rtMod

Details

[gS] indicates grid search will be performed automatically if more than one value is passed

Description

Usage

Arguments

Value

Details

See Also