srlTS {srlTS}

Perform time series ranked sparsity methods

Description

Perform time series ranked sparsity methods

Usage

srlTS(
  y,
  X = NULL,
  n_lags_max,
  gamma = c(0, 2^(-2:4)),
  ptrain = 0.8,
  pf_eps = 0.01,
  w_endo,
  w_exo,
  ncvreg_args = list(penalty = "lasso", returnX = FALSE, lambda.min = 0.001)
)

## S3 method for class 'srlTS'
plot(x, log.l = TRUE, ...)

## S3 method for class 'srlTS'
coef(object, choose = c("AICc", "BIC", "all"), ...)

## S3 method for class 'srlTS'
print(x, ...)

## S3 method for class 'srlTS'
summary(object, ...)

Arguments

y

univariate time series outcome

X

matrix of predictors (no intercept)

n_lags_max

maximum number of lags to consider

gamma

vector of exponent for weights

ptrain

prop. to leave out for test data

pf_eps

penalty factors below this will be set to zero

w_endo

optional pre-specified weights for endogenous terms

w_exo

optional pre-specified weights for exogenous terms (see details)

ncvreg_args

additional args to pass through to ncvreg

x

a srlTS object

log.l

Should the x-axis (lambda) be logged?

...

passed to downstream functions

object

a srlTS object

choose

which criterion to use for lambda selection (AICc, BIC, or all)

Details

The default weights for exogenous features will be chosen based on a similar approach to the adaptive lasso (using bivariate OLS estimates). For lower dimensional X, it's advised to set w_exo="unpenalized", because this allows for statistical inference on exogenous variable coefficients via the summary function.

Value

A list of class slrTS with elements

fits

a list of lasso fits

ncvreg_args

arguments passed to ncvreg

gamma

the (negative) exponent on the penalty weights, one for each fit

n_lags_max

the maximum number of lags

y

the time series

X

the utilized matrix of exogenous features

oos_results

results on test data using best of fits

train_idx

index of observations used in training data

x invisibly

a vector of model coefficients

x (invisibly)

the summary object produced by ncvreg evaluated at the best tuning parameter combination (best AICc).

References

Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.

Peterson, R.A., Cavanaugh, J.E. Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials. AStA Adv Stat Anal (2022). https://doi.org/10.1007/s10182-021-00431-7

See Also

predict.srlTS

Examples

data("LakeHuron")
fit_LH <- srlTS(LakeHuron)
fit_LH
coef(fit_LH)
plot(fit_LH)