rcppmlpack2: Rcpp Integration for MLPACK Library

Description Usage Arguments Details Value Examples

Run a lars/lasso/elasticNet regression

1	LARS(matX, vecY, lambda1, lambda2, useCholesky = FALSE, testX = NULL)

`matX`	A matrix of explanatory variables, i.e. regressors
`vecY`	A vector with the target variable
`lambda1`	A doube with l1-norm penalty regularization parameter
`lambda2`	A doube with l2-norm penalty regularization parameter
`useCholesky`	A logical value indicating whether to use the Cholesky
`testX`	A optional matrix of test values to validate prediction decomposition when solving the linear system, else full Gram matrix is used.

This function performs a lars, lasso or elastic net regression.

A list with estimated coefficient, the value of lambda1 after each iteration and the predicted values, either from the training data or, if supplied, the test set.

## LARS demo data set from MLPACK with limited rank
data(lars)
fit1 <- LARS(matX = lars_dependent_x, vecY = lars_dependent_y, 0.1, 0.1, FALSE)
fit2 <- LARS(matX = lars_dependent_x, vecY = lars_dependent_y, 0.1, 0.1, TRUE)