s_PPR: Projection Pursuit Regression (PPR) [R]

In egenn/rtemis: Machine Learning and Visualization

Projection Pursuit Regression (PPR) [R]

Description

Train a Projection Pursuit Regression model

Usage

s_PPR(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  grid.resample.params = setup.grid.resample(),
  gridsearch.type = c("exhaustive", "randomized"),
  gridsearch.randomized.p = 0.1,
  weights = NULL,
  nterms = NULL,
  max.terms = nterms,
  optlevel = 3,
  sm.method = "spline",
  bass = 0,
  span = 0,
  df = 5,
  gcvpen = 1,
  metric = "MSE",
  maximize = FALSE,
  n.cores = rtCores,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  trace = 0,
  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
grid.resample.params	List: Output of setup.resample defining grid search parameters.
gridsearch.type	Character: Type of grid search to perform: "exhaustive" or "randomized".
gridsearch.randomized.p	Float (0, 1): If gridsearch.type = "randomized", randomly test this proportion of combinations.
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.
nterms	[gS] Integer: number of terms to include in the final model
max.terms	Integer: maximum number of terms to consider in the model
optlevel	[gS] Integer [0, 3]: optimization level (Default = 3). See Details in stats::ppr
sm.method	[gS] Character: "supsmu", "spline", or "gcvspline". Smoothing method. Default = "spline"
bass	[gS] Numeric [0, 10]: for sm.method = "supsmu": larger values result in greater smoother. See stats::ppr
span	[gS] Numeric [0, 1]: for sm.method = "supsmu": 0 (Default) results in automatic span selection by local crossvalidation. See stats::ppr
df	[gS] Numeric: for sm.method = "spline": Specify smoothness of each ridge term. See stats::ppr
gcvpen	[gs] Numeric: for sm.method = "gcvspline": Penalty used in the GCV selection for each degree of freedom used. Higher values result in greater smoothing. See stats::ppr.
metric	Character: Metric to minimize, or maximize if maximize = TRUE during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.
maximize	Logical: If TRUE, metric will be maximized if grid search is run.
n.cores	Integer: Number of cores to use.
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.
trace	Integer: If greater than 0, print additional information to console
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 ppr

Details

[gS]: If more than one value is passed, parameter tuning via grid search will be performed on resamples of the training set prior to training model on full training set Interactions: PPR automatically models interactions, no need to specify them

Value

Object of class rtMod

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

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_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.