slise.fit: SLISE for robust regression.

In edahelsinki/slise: Sparse Linear Subset Explanations

SLISE for robust regression.

Description

It is highly recommended that you normalise the data, either before using SLISE or by setting normalise = TRUE.

Usage

slise.fit(
  X,
  Y,
  epsilon,
  lambda1 = 0,
  lambda2 = 0,
  weight = NULL,
  intercept = TRUE,
  normalise = FALSE,
  initialisation = slise_initialisation_candidates,
  ...
)

Arguments

X	Matrix of independent variables
Y	Vector of the response variable
epsilon	Error tolerance
lambda1	L1 regularisation coefficient (default: 0)
lambda2	L2 regularisation coefficient (default: 0)
weight	Optional weight vector (default: NULL)
intercept	Should an intercept be added (default: TRUE)
normalise	Preprocess X and Y by scaling, note that epsilon is not scaled (default: FALSE)
initialisation	Function that gives the initial alpha and beta, or a list containing the initial alpha and beta (default: slise_initialisation_candidates)
...	Arguments passed on to graduated_optimisation, slise_initialisation_candidates beta_max Stopping sigmoid steepness (default: 20 / epsilon^2) max_approx Approximation ratio when selecting the next beta (default: 1.15) max_iterations Maximum number of OWL-QN iterations (default: 300) debug Should debug statement be printed each iteration (default: FALSE) num_init the number of initial subsets to generate (default: 500) beta_max_init the maximum sigmoid steepness in the initialisation pca_treshold the maximum number of columns without using PCA (default: 10)

Value

slise.object

Examples

X <- matrix(rnorm(32), 8, 4)
Y <- rnorm(8)
model <- slise.fit(X, Y, (max(Y) - min(Y)) * 0.1)
predicted <- predict(model, X)

edahelsinki/slise documentation built on Aug. 24, 2023, 11:03 p.m.