s_QRNN: Quantile Regression Neural Network [R]
In egenn/rtemis: Machine Learning and Visualization

s_QRNN

R Documentation

Quantile Regression Neural Network [R]

Description

Train an ensemble of Neural Networks to perform Quantile Regression using qrnn

Usage

s_QRNN(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  n.hidden = 1,
  tau = 0.5,
  n.ensemble = 5,
  iter.max = 5000,
  n.trials = 5,
  bag = TRUE,
  lower = -Inf,
  eps.seq = 2^(-8:-32),
  Th = qrnn::sigmoid,
  Th.prime = qrnn::sigmoid.prime,
  penalty = 0,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  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
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`n.hidden`	Integer: Number of hidden nodes.
`tau`	Numeric: tau-quantile.
`n.ensemble`	Integer: Number of NNs to train.
`iter.max`	Integer: Max N of iteration of the optimization algorithm.
`n.trials`	Integer: N of trials. Used to avoid local minima.
`bag`	Logical: If TRUE, use bagging.
`lower`	Numeric: Left censoring point.
`eps.seq`	Numeric: sequence of eps values for the finite smoothing algorithm.
`Th`	Function: hidden layer transfer function; use `qrnn::sigmoid`, `qrnn::elu`, or `qrnn::softplus` for a nonlinear model and `qrnn::linear` for a linear model.
`Th.prime`	Function: derivative of hidden layer transfer function.
`penalty`	Numeric: weight penalty for weight decay regularization.
`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"
`question`	Character: 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 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 arguments to be passed to `qrnn::qrnn.fit`.

Details

For more details on hyperparameters, see qrnn::qrnn.fit

Author(s)

E.D. Gennatas

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()