s.PPR: Projection Pursuit Regression (PPR) [R]

Description

Train a Projection Pursuirt Regression model

Usage

s.PPR(x, y = NULL, x.test = NULL, y.test = NULL, x.name = NULL,
  y.name = NULL, grid.resample.rtset = rtset.grid.resample(),
  grid.search.type = c("exhaustive", "randomized"),
  grid.search.randomized.p = 0.1, weights = NULL, nterms = NULL,
  max.terms = nterms, optlevel = 3, sm.method = c("supsmu", "spline",
  "gcvspline"), bass = 0, span = 0, df = 5, gcvpen = 1,
  metric = "MSE", maximize = FALSE, n.cores = rtCores,
  print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  rtclass = NULL, verbose = TRUE, trace = 0, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)

Arguments

Numeric vector or matrix / data frame of features i.e. independent variables

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

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

nterms

[gS] Integer: number of terms to include in the final model

optlevel

[gS] Integer [0, 3]: optimization level (Default = 3). See Details in stats::ppr

sm.method

[gS] String: "supsmu", "spline", "gcvspline". Smoothing method. Default = "gcvspline"

bass

[gS] Numeric [0, 10]: for sm.method "supsmu": larger values result in greater smoother (Default = 5). 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

[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 -> greater smoothing. See stats::ppr Default = 5

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

String: "zero", "dark", "box", "darkbox"

question

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

rtclass

String: Class type to use. "S3", "S4", "RC", "R6"

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If higher than 0, will print more information to the console. Default = 0

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 arguments to be passed to ppr

Value

Object of class rtemis

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

Description

Usage

Arguments

Value

Details

See Also