Learn R Programming
rtemis (version 0.79)

s.BART: Bayesian Additive Regression Trees [C, R]

Description

Trains a Bayesian Additive Regression Tree (BART) model using package bartMachine and validates it

Usage

s.BART(x, y = NULL, x.test = NULL, y.test = NULL, x.name = NULL,
  y.name = NULL, n.trees = c(100, 200), k_cvs = c(2, 3),
  nu_q_cvs = list(c(3, 0.9), c(10, 0.75)), k_folds = 5,
  n.burnin = 250, n.iter = 1000, n.cores = rtCores,
  upsample = FALSE, upsample.seed = NULL, print.plot = TRUE,
  plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  rtclass = NULL, verbose = TRUE, trace = 0, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), java.mem.size = 12,
  ...)

Arguments

x

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

y

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

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)

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.

rtclass

String: Class type to use. "S3", "S4", "RC", "R6"

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If higher than 0, will print more information to the console. Default = 0

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, sets bartMachine's serialize to TRUE and saves model to outdir

...

Additional arguments to be passed to bartMachine::bartMachine

Value

Object of class rtemis

Details

If you are having trouble with rJava in Rstudio on macOS, see my solution here: https://support.rstudio.com/hc/en-us/community/posts/203663956/comments/249073727 bartMachine does not support case weights

See Also

elevate for external cross-validation

Other Supervised Learning: s.ADABOOST, s.ADDTREE, s.BAYESGLM, s.BRUTO, s.C50, s.CART, s.CTREE, s.DA, s.ET, s.EVTREE, s.GAM.default, s.GAM.formula, s.GAMSEL, s.GAM, s.GBM3, s.GBM, s.GLMNET, s.GLM, s.GLS, s.H2ODL, s.H2OGBM, s.H2ORF, s.IRF, s.KNN, s.LDA, s.LM, s.MARS, s.MLRF, s.MXN, s.NBAYES, s.NLA, s.NLS, s.NW, s.POLYMARS, s.PPR, s.PPTREE, s.QDA, s.QRNN, s.RANGER, s.RFSRC, s.RF, s.SGD, s.SPLS, s.SVM, s.TFN, s.XGBLIN, s.XGB

Other Tree-based methods: s.ADABOOST, s.ADDTREE, s.C50, s.CART, s.CTREE, s.ET, s.EVTREE, s.GBM3, s.GBM, s.H2OGBM, s.H2ORF, s.IRF, s.MLRF, s.PPTREE, s.RANGER, s.RFSRC, s.RF, s.XGB