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 (number between 0 and 1)
- prob.separate
probability of separate effect (number between 0 and 1)
- 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 a list with slots
y_train (\(n_0 \times q\) matrix),
X_train(\(n_0 \times p\) matrix),
y_test (\(n_1 \times q\) matrix),
X_test (\(n_1 \times p\) matrix),
and beta (\(p \times q\) 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