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Simulate target grouped data for testing with ptLasso.

makedata.targetgroups.Rd

Simulate target grouped data for testing with ptLasso.

Usage

makedata.targetgroups(
  n,
  p,
  scommon,
  sindiv,
  class.sizes,
  shift.common,
  shift.indiv
)

Arguments

n

Total number of observations to simulate.

p

Total number of features to simulate.

scommon

Number of features shared by all groups.

sindiv

Vector of length k. The i^th entry indicates the number of features specific to group i.

class.sizes

Vector of length k. The i^th entry indicates the number of observations in group i.

shift.common

A list of length k (one for each group). Each entry of this list should be a vector of length scommon, containing the shifts for the scommon features. The i^th entry of this list will be added to the first scommon columns of x for observations in group i.

shift.indiv

The shifts for the individual features, in the same form as shift.common.

Value

A list:

x

Simulated features, size n x p.

y

Outcomes y, length n.

See also

cv.ptLasso, ptLasso.

Author

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

Examples


k = 5
class.sizes = rep(50, k)
n = sum(class.sizes)
scommon = 3
sindiv = rep(3, k)
p = 3*(sum(sindiv) + scommon)
shift.common  = lapply(seq(-.1, .1, length.out = k), function(i) rep(i, scommon))
shift.indiv = lapply(1:k, function(i) -shift.common[[i]])

out = makedata.targetgroups(n=n, p=p, scommon=scommon,
                            sindiv=sindiv, class.sizes=class.sizes,
                            shift.common=shift.common, shift.indiv=shift.indiv)
x = out$x; y=out$y