sparsereg3D.ncv: Model Evaluation with Nested Cross-Validation

Description Usage Arguments Value

View source: R/sparsereg3D.ncv.r

Description

Model Evaluation with Nested Cross-Validation

Usage

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sparsereg3D.ncv(sparse.reg, lambda, step = FALSE, ols = FALSE,
  all = FALSE, lambda.1se = FALSE, w = NULL, alpha = 1)

Arguments

sparse.reg

output from pre.sparsereg3D function

lambda

vector of regularization parameter values for lasso regression

step

logical. If TRUE stepwise procedure will be used when fitting OLS model

ols

logical. If TRUE model will be fitted with OLS insted of using lasso

all

logical. If TRUE a detailed output will be prepared.

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).

w

weighted parameter (positive number) that controls the level of using observations from deeper layers as less informative. General weighted model is $w=1/(1+w*depth)$. This option is still under development, so it is not included in the function for model selection.

alpha

ElasticNet parameter. see glmnet function in glmnet package. Default is alpha = 1 indicating that the lasso penalty is used.

seed

random number generator

Value

List of objects including:

@keywords Model evaluation


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