s_LIHADBoost: Boosting of Linear Hard Additive Trees [R]
In egenn/rtemis: Machine Learning and Visualization

s_LIHADBoost

R Documentation

Boosting of Linear Hard Additive Trees [R]

Description

Boost a Linear Hard Additive Tree (i.e. LIHAD, i.e. LINAD with hard splits)

Usage

s_LIHADBoost(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  resid = NULL,
  boost.obj = NULL,
  learning.rate = 0.5,
  case.p = 1,
  max.depth = 5,
  gamma = 0.1,
  alpha = 0,
  lambda = 1,
  lambda.seq = NULL,
  minobsinnode = 2,
  minobsinnode.lin = 10,
  shrinkage = 1,
  part.minsplit = 2,
  part.xval = 0,
  part.max.depth = 1,
  part.cp = 0,
  part.minbucket = 5,
  lin.type = c("glmnet", "cv.glmnet", "lm.ridge", "allSubsets", "forwardStepwise",
    "backwardStepwise", "glm", "sgd", "solve", "none"),
  cv.glmnet.nfolds = 5,
  which.cv.glmnet.lambda = "lambda.min",
  max.iter = 10,
  tune.n.iter = TRUE,
  earlystop.params = setup.earlystop(),
  lookback = TRUE,
  init = NULL,
  .gs = FALSE,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = "exhaustive",
  metric = NULL,
  maximize = NULL,
  cxrcoef = FALSE,
  print.progress.every = 5,
  print.error.plot = "final",
  x.name = NULL,
  y.name = NULL,
  question = NULL,
  base.verbose = FALSE,
  verbose = TRUE,
  grid.verbose = FALSE,
  trace = 0,
  prefix = NULL,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  print.plot = FALSE,
  print.base.plot = FALSE,
  print.tune.plot = TRUE,
  plot.type = "l",
  save.gridrun = FALSE,
  outdir = NULL,
  n.cores = rtCores,
  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
`learning.rate`	Float (0, 1] Learning rate for the additive steps
`max.iter`	Integer: Maximum number of iterations (additive steps) to perform. Default = 10
`init`	Float: Initial value for prediction. Default = mean(y)
`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`	Character: 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`	Character: "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 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 parameters to be passed to learner