rtemis (version 0.79)

s.CTREE: Conditional Inference Trees [C, R, S]

Description

Train a conditional inference tree using partykit::ctree

Usage

s.CTREE(x, y = NULL, x.test = NULL, y.test = NULL, weights = NULL,
  control = partykit::ctree_control(), ipw = TRUE, ipw.type = 2,
  upsample = FALSE, x.name = NULL, y.name = NULL,
  print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  verbose = TRUE, outdir = NULL, save.mod = ifelse(!is.null(outdir),
  TRUE, FALSE), ...)

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

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

control

List of parameters for the CTREE algorithms. Set using partykit::ctree_control

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

x.name

Character: Name for feature set

y.name

Character: Name for outcome

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.

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)

...

Additional arguments

Value

rtMod object

See Also

elevate

Other Supervised Learning: s.ADABOOST, s.ADDTREE, s.BART, s.BAYESGLM, s.BRUTO, s.C50, s.CART, 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.BART, s.C50, s.CART, 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