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Simulate input grouped data for testing with ptLasso.

makedata.Rd

Simulate input grouped data for testing with ptLasso.

Usage

makedata(
  n,
  p,
  k,
  scommon,
  sindiv,
  class.sizes,
  beta.common,
  beta.indiv,
  intercepts = rep(0, k),
  sigma = 0,
  outcome = c("gaussian", "binomial", "multinomial"),
  mult.classes = 3
)

Arguments

n

Total number of observations to simulate.

p

Total number of features to simulate.

k

Number of groups.

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.

beta.common

The coefficients for the common features. This can be a vector of length k, in which case, the i^th entry is the coefficient for all scommon features for group i. This can alternatively be a list of length k (one for each group). Each entry of this list should be a vector of length scommon, containing the coefficients for the scommon features.

beta.indiv

The coefficients for the individual features, in the same form as beta.common.

intercepts

A vector of length k, indicating the intercept for each group. Default is 0.

sigma

Only used for the Gaussian outcome. Should be a number greater than or equal to 0, used to modify the amount of noise added. Default is 0.

outcome

May be '"gaussian"', '"binomial"' or '"multinomial"'.

mult.classes

Number of classes to simulate for the multinomial setting.

Value

A list:

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


# Data with a binary outcome:
k = 3
class.sizes = rep(100, k)
n = sum(class.sizes)
scommon = 5
sindiv = rep(5, k) 
p = 2*(sum(sindiv) + scommon)
beta.common = lapply(1:k, function(i)  c(-.5, .5, .3, -.9, .1))
beta.indiv = lapply(1:k, function(i)  0.9 * beta.common[[i]])

out = makedata(n=n, p=p, k=k, scommon=scommon, sindiv=sindiv,
               beta.common=beta.common, beta.indiv=beta.indiv,
               class.sizes=class.sizes, outcome="binomial")
x = out$x; y=out$y; groups = out$group