train_lasso: Do parameter tuning for Lasso and Adaptive lasso

In zhan-gao/LasForecast: Time series linear predictive regression

Do parameter tuning for Lasso and Adaptive lasso

Description

Do parameter tuning for Lasso and Adaptive lasso

Usage

train_lasso(
  x,
  y,
  ada = TRUE,
  gamma = 1,
  intercept = TRUE,
  scalex = FALSE,
  lambda_seq = NULL,
  train_method = "timeslice",
  nlambda = 100,
  lambda_min_ratio = 1e-04,
  k = 10,
  initial_window = ceiling(nrow(x) * 0.7),
  horizon = 1,
  fixed_window = TRUE,
  skip = 0
)

Arguments

x	Predictor matrix (n-by-p matrix)
y	Response variable
ada	A boolean: Do parameter tuning for adaptive Lasso if TRUE (Default) For Lasso if FALSE.
gamma	Parameter controlling the inverse of first step estimate. By default = 1.
intercept	A boolean: include an intercept term or not
scalex	A boolean: standardize the design matrix or not
lambda_seq	Candidate sequnece of parameters. If NULL, the function generates the sequnce.
train_method	"timeslice", "cv", "cv_random", "aic", "bic", "aicc", "hqc" "timeslice": https://topepo.github.io/caret/data-splitting.html#time By combining initial window, horizon, fixed window and skip, we can control the sample splitting. Roll_block: Setting initial_window = horizon = floor(nrow(x) / k), fixed_window = False, and skip = floor(nrow(x) / k) - 1 Period-by-period rolling: skip = 0. "cv": Cross-validation based on block splits. "cv_random": Cross-validition based on random splits. "aic", "bic", "aicc", "hqc": based on information criterion.
nlambda	# of lambdas
lambda_min_ratio	# lambda_min_ratio * lambda_max = lambda_min
k	k-fold cv if "cv" is chosen
initial_window	control "timeslice"
horizon	control "timeslice"
fixed_window	control "timeslice"
skip	control "timeslice"

Value

bestTune

Examples

train_lasso(x,y)

zhan-gao/LasForecast documentation built on Sept. 18, 2024, 9:40 p.m.