uniLasso (version 2.11)

simulate_uniLasso: Simulate data for use in uniLasso and uniReg

Description

We use some standard examples in our uniLasso paper, and for convenience we provide generators for these datasets.

Usage

simulate_uniLasso(
  example = c("low-SNR", "medium-SNR", "high-SNR", "home-court", "two-class",
    "counter-example"),
  wide = TRUE
)

Value

a list with components "x", "y", "xtest", "ytest", "mutest", and "sigma", where "mutest" is the true test mean, and "ytest <- mutest + rnorm(nrow(xtest))*sigma."

Arguments

example

which of the prepackaged examples to use. Choices are "low-SNR","medium-SNR","high-SNR","home-court","two-class","counter-example", as described in the uniLasso paper. The three SNRs used are 0.5 (low), 1.0 (medium) and 2.0 (high) (also used for home-court). The training sizes for the first four are 300, and test sizes 3000.

wide

logical variable which determines if p>n (default, 1000) or not (100). This function calls worker functions simulate_gaussian(), simulate_two-class(), and simulate_counterexample(), which are currently not documented.

Examples

dat = simulate_uniLasso("high-SNR")
fit = cv.uniLasso(dat$x, dat$y)
mse = mean( (predict(fit, dat$xtest)- dat$mutest)^2)