Simulate input grouped data (gaussian outcome) for testing with ptLasso.

No required arguments; used primarily for documentation. Simply calls makedata with a reasonable set of features.

Usage

gaussian.example.data(
  k = 5,
  class.sizes = rep(200, k),
  n = sum(class.sizes),
  scommon = 10,
  sindiv = rep(10, k),
  p = 2 * (sum(sindiv) + scommon),
  beta.common = 3 * (1:k),
  beta.indiv = rep(3, k),
  intercepts = rep(0, k),
  sigma = 20
)

Arguments

k

Default: 5.

class.sizes

Default: rep(200, k).

n

Default: sum(class.sizes).

scommon

Default: 10.

sindiv

Default: rep(10, k).

p

Default: 2*(sum(sindiv) + scommon).

beta.common

Default: 3*(1:k).

beta.indiv

Default: rep(3, k).

intercepts

Default: rep(0, k).

sigma

Default: 20.

Value

A list for data with 5 groups and a gaussian outcome, n=1000 and p=120:

x

Simulated features, size n x p.

y

Outcomes y, length n.

groups

Vector of length n, indicating which observations belong to which group.

snr

Gaussian outcome only: signal to noise ratio.

mu

Gaussian outcome only: the value of y before noise is added.

See also

cv.ptLasso, ptLasso.

Author

Erin Craig and Rob Tibshirani
Maintainer: Erin Craig <erincr@stanford.edu>

Examples

out = gaussian.example.data()
x = out$x; y=out$y; groups = out$group