s_LINOA: Linear Optimized Additive Tree (C, R)
In egenn/rtemis: Machine Learning and Visualization

s_LINOA

R Documentation

Linear Optimized Additive Tree (C, R)

Description

Train a Linear Optimized Additive Tree

Usage

s_LINOA(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  max.leaves = 8,
  learning.rate = 0.5,
  select.leaves.smooth = TRUE,
  force.max.leaves = NULL,
  lookback = TRUE,
  gamma = 0,
  n.quantiles = 20,
  minobsinnode = NULL,
  minbucket = NULL,
  lin.type = c("forwardStepwise", "glmnet", "cv.glmnet", "lm.ridge", "allSubsets",
    "backwardStepwise", "glm", "solve", "none"),
  alpha = 1,
  lambda = 0.05,
  lambda.seq = NULL,
  cv.glmnet.nfolds = 5,
  which.cv.glmnet.lambda = "lambda.min",
  nbest = 1,
  nvmax = 3,
  .rho = TRUE,
  rho.max = 1000,
  init = NULL,
  metric = "auto",
  maximize = NULL,
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = "exhaustive",
  save.gridrun = FALSE,
  grid.verbose = verbose,
  keep.x = FALSE,
  simplify = TRUE,
  cxrcoef = FALSE,
  n.cores = rtCores,
  splitline.cores = 1,
  .preprocess = NULL,
  plot.tuning = TRUE,
  verbose.predict = FALSE,
  x.name = NULL,
  y.name = NULL,
  question = NULL,
  outdir = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  save.mod = FALSE,
  .gs = FALSE,
  verbose = TRUE,
  trace = 1
)

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 `ifw`, therefore set `weights = NULL` if using `ifw`. Note: If `weight` are provided, `ifw` is not used. Leave NULL if setting `ifw = TRUE`.
`ifw`	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If `weights` are provided, `ifw` is not used.
`ifw.type`	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
`upsample`	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: 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
`downsample`	Logical: If TRUE, downsample majority class to match size of minority class
`resample.seed`	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
`max.leaves`	Integer: Maximum number of terminal nodes to grow
`lookback`	Logical: If TRUE, check validation error to decide when to stop growing tree. Default = FALSE
`minobsinnode`	Integer: Minimum N observations needed in node, before considering splitting
`lambda`	Float: lambda parameter for `MASS::lm.ridge` Default = .01
`nvmax`	[gS] Integer: Number of max features to use for lin.type "allSubsets", "forwardStepwise", or "backwardStepwise". If values greater than n of features in `x` are provided, they will be excluded
`init`	Initial value. Default = `mean(y)`
`plot.tuning`	Logical: If TRUE, plot validation error during gridsearch
`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.
`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
`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`	Character: "zero", "dark", "box", "darkbox"
`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)`
`.gs`	internal use only
`verbose`	Logical: If TRUE, print summary to screen.
`trace`	Integer: If higher than 0, will print more information to the console.

Details

The Linear Optimized Additive Tree grows a tree by finding splits that minimize loss after linear models are fit on each child. We specify an upper threshold of leaves using max.leaves instead of directly defining a number, because depending on the other parameters and the datasets, splitting may stop early.