s_SPLS: Sparse Partial Least Squares Regression (C, R)

In egenn/rtemis: Machine Learning and Visualization

Sparse Partial Least Squares Regression (C, R)

Description

Train an SPLS model using spls::spls (Regression) and spls::splsda (Classification)

Usage

s_SPLS(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  upsample = TRUE,
  downsample = FALSE,
  resample.seed = NULL,
  k = 2,
  eta = 0.5,
  kappa = 0.5,
  select = "pls2",
  fit = "simpls",
  scale.x = TRUE,
  scale.y = TRUE,
  maxstep = 100,
  classifier = c("lda", "logistic"),
  grid.resample.params = setup.resample("kfold", 5),
  gridsearch.type = c("exhaustive", "randomized"),
  gridsearch.randomized.p = 0.1,
  metric = NULL,
  maximize = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  trace = 0,
  grid.verbose = verbose,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  n.cores = rtCores,
  ...
)

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
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)
k	[gS] Integer: Number of components to estimate.
eta	[gS] Float [0, 1): Thresholding parameter.
kappa	[gS] Float [0, .5]: Only relevant for multivariate responses: controls effect of concavity of objective function.
select	[gS] Character: "pls2", "simpls". PLS algorithm for variable selection.
fit	[gS] Character: "kernelpls", "widekernelpls", "simpls", "oscorespls". Algorithm for model fitting.
scale.x	Logical: if TRUE, scale features by dividing each column by its sample standard deviation
scale.y	Logical: if TRUE, scale outcomes by dividing each column by its sample standard deviation
maxstep	[gS] Integer: Maximum number of iteration when fitting direction vectors.
classifier	Character: Classifier used by spls::splsda "lda" or "logistic":
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.
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.
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	If > 0 print diagnostic messages
grid.verbose	Logical: Passed to gridSearchLearn
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)
n.cores	Integer: Number of cores to be used by gridSearchLearn
...	Additional parameters to be passed to npreg

Details

[gS] denotes argument can be passed as a vector of values, which will trigger a grid search using gridSearchLearn np::npreg allows inputs with mixed data types.

Value

Object of class rtemis

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_Isotonic(), 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_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Examples

## Not run: 
x <- rnorm(100)
y <- .6 * x + 12 + rnorm(100)
mod <- s_SPLS(x, y)

## End(Not run)

egenn/rtemis documentation built on Dec. 17, 2024, 6:16 p.m.