lasso_cv_search: Search for optimal lambda via cross-validation

In be-green/quantspace: Quantile Regression via Quantile Spacing

Search for optimal lambda via cross-validation

Description

Search for optimal lambda via cross-validation

Usage

lasso_cv_search(
  x,
  y,
  tau = 0.5,
  weights = NULL,
  method = "two_pass",
  intercept = TRUE,
  nfolds = 10,
  foldid = NULL,
  nlambda = 100,
  eps = 1e-04,
  init.lambda = 2,
  parallel = T,
  coef.cutoff = 1e-05,
  thresh = 0.01,
  ...
)

Arguments

x	design matrix for regression
y	outcome variable for regression
tau	target quantile
weights	optional weights for regression
method	method to be used for penalized quantile regression (usually one of "sfn" or "br")
intercept	Whether to model the intercept or not
nfolds	number of folds to use for crossvalidation
foldid	optional pre-specified fold identifier (for example, if you want the folds to satisfy underlying data groupings)
nlambda	number of lambdas to search over
eps	smallest lambda used in search
init.lambda	initial lambda for search
parallel	whether to run cv scoring in parallel or not
coef.cutoff	what cutoff to use for "0" coefficients
thresh	threshhold for what counts as a "sparse enough" solution for the top of the grid
...	other parameters to pass on to fitting method

Details

Searches for a range of lambdas by first expanding the max lambda if necessary, then finding the smallest lambda that sets all coefficients to zero. Then it computes kfold CV scores along a grid of lambdas, returning the scores and the smallest lambda.

be-green/quantspace documentation built on March 20, 2024, 5:30 p.m.