Simulate input grouped data (binomial outcome) for testing with ptLasso.
binomial.example.data.RdNo required arguments; used primarily for documentation. Simply calls makedata with a reasonable set of features.
Usage
binomial.example.data(
k = 3,
class.sizes = rep(100, k),
n = sum(class.sizes),
scommon = 5,
sindiv = rep(5, k),
p = 2 * (sum(sindiv) + scommon),
beta.common = list(c(-0.5, 0.5, 0.3, -0.9, 0.1), c(-0.3, 0.9, 0.1, -0.1, 0.2), c(0.1,
0.2, -0.1, 0.2, 0.3)),
beta.indiv = lapply(1:k, function(i) 0.9 * beta.common[[i]]),
intercepts = rep(0, k),
sigma = NULL
)Arguments
- k
Default: 3.
- class.sizes
Default: rep(100, k).
- n
Default: sum(class.sizes).
- scommon
Default: 5.
- sindiv
Default: rep(5, k).
- p
Default: 2*(sum(sindiv) + scommon).
- beta.common
Default: list(c(-.5, .5, .3, -.9, .1), c(-.3, .9, .1, -.1, .2), c(0.1, .2, -.1, .2, .3)).
- beta.indiv
Default: lapply(1:k, function(i) 0.9 * beta.common[[i]]).
- intercepts
Default: rep(0,k).
- sigma
Default: NULL.
Value
A list for data with 5 groups and a binomial outcome, n=300 and p=40:
- 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.