s.ADDTBOOST: Boosting of Additive Trees [R]

Description

Boost an additive tree using addtboost

Usage

s.ADDTBOOST(x, y = NULL, x.test = NULL, y.test = NULL,
  x.valid = NULL, y.valid = NULL, resid = NULL, boost.obj = NULL,
  mod.params = rtset.ADDT(), case.p = 1, weights = NULL,
  max.iter = 10, learning.rate = 0.1, init = mean(y),
  cxrcoef = FALSE, print.progress.every = 5,
  print.error.plot = "final", x.name = NULL, y.name = NULL,
  question = NULL, base.verbose = FALSE, verbose = TRUE, trace = 0,
  prefix = NULL, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"),
  print.plot = TRUE, print.base.plot = FALSE, plot.type = "l",
  outdir = NULL, save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)

Arguments

Data frame: Input features

Vector: Output

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

mod.params

Named list of arguments for mod

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

max.iter

Integer: Maximum number of iterations (additive steps) to perform. Default = 10

learning.rate

Float (0, 1] Learning rate for the additive steps

init

Float: Initial value for prediction. Default = mean(y)

cxrcoef

Logical: If TRUE, pass cxr = TRUE, cxrcoef = TRUE to predict.addTreeRaw

print.error.plot

String or Integer: "final" plots a training and validation (if available) error curve at the end of training. If integer, plot training and validation error curve every this many iterations during training

x.name

Character: Name for feature set

y.name

Character: Name for outcome

question

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

base.verbose

Logical: verbose argument passed to learner

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If > 0, print diagnostic info to console

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"

print.plot

Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted

print.base.plot

Logical: Passed to print.plot argument of base learner, i.e. if TRUE, print error plot for each base learner

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 parameters to be passed to learner

tolerance

Float: If training error <= this value, training stops

tolerance.valid

Float: If validation error <= this value, training stops