sis_lasso: Sure independence screening followed by lasso
In hugogogo/sprintr: Sparse Reluctant Interaction Modeling

Description Usage Arguments Value Examples

Sure independence screening followed by lasso

sis_lasso(
  x,
  y,
  num_keep = NULL,
  lam_min_ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04),
  nfold = 5,
  foldid = NULL,
  ...
)

`x`	An `n` by `p` design matrix of main effects. Each row is an observation of `p` main effects.
`y`	A response vector of size `n`.
`num_keep`	Number of variables to keep in the screening phase
`...`	other arguments to be passed to the `glmnet` calls, such as `alpha` or `penalty.factor`

An object of S3 class "cv.hier".

n: The sample size.
p: The number of main effects.
fit: The whole cv.glmnet fit object.
compact: A compact representation of the selected variables. compact has three columns, with the first two columns representing the indices of a selected variable (main effects with first index = 0), and the last column representing the estimate of coefficients.

set.seed(123)
n <- 100
p <- 200
# dense input
x <- matrix(rnorm(n * p), n, p)
y <- x[, 1] - 2 * x[, 2] + 3 * x[, 1] * x[, 3] - 4 * x[, 4] * x[, 5] + rnorm(n)
mod <- hier_lasso(x = x, y = y)