Simulates data for multi-task learning.
Usage
sim.data.multiple(
prob.common = 0.05,
prob.separate = 0.05,
q = 3,
n0 = 100,
n1 = 10000,
p = 200,
rho = 0.5,
family = "gaussian"
)Arguments
- prob.common
probability of common effect
- prob.separate
probability of separate effect
- q
number of datasets: integer
- n0
number of training samples: integer vector of length q
- n1
number of testing samples for all datasets: integer
- p
number of features: integer
- rho
correlation (for decreasing structure)
- family
character "gaussian" or "binomial"
Value
Returns list with slots y_train (n0 x q matrix), X_train (n0 x p matrix), y_test (n1 x q matrix), X_test (n1 x p matrix), and beta (p x q matrix).
Examples
data <- sim.data.multiple()
sapply(X=data,FUN=dim)
#> y_train X_train y_test X_test beta
#> [1,] 100 100 10000 10000 200
#> [2,] 3 200 3 200 3