sparsereg3D.sel: Model Selection based on Cross-Validation

In pejovic/sparsereg3D: An Implementation of Lasso Regression for 3D Soil Mapping.

Description

Model Selection based on Cross-Validation

Usage

sparsereg3D.sel(sparse.reg, lambda = 0, ols = FALSE, step = FALSE,
  lambda.1se = FALSE)

Arguments

sparse.reg	output from pre.sparsereg3D function
lambda	vector of regularization parameter values for lasso regression
ols	logical. If TRUE model will be fitted with OLS insted of using lasso
step	logical. If TRUE stepwise procedure will be used when fitting OLS model
lambda.1se	logical. If TRUE one sigma lambda rule will be used (largest lambda value with cv.err less than or equal to min(cv.err)+ SE).
seed	random number generator

Value

List of objects including:

• model: list of objects containing model description

• lambda: Regularization parameter value for lasso models

• coefficients: Model coefficients

• std.param: Standardization parameters

• comp: logical. Indicate whether the compositional variable is modeled or not.

@keywords Model selection

pejovic/sparsereg3D documentation built on May 25, 2019, 12:45 a.m.