Simulates data for transfer learning.
Usage
sim.data.transfer(
prob.common = 0.05,
prob.separate = 0.05,
q = 3,
n0 = c(50, 100, 200),
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 (\(q\) vectors)
and X_train (\(q\) matrices with \(p\) columns) for training data,
and y_test (\(vectors\)) and X_test
(\(q\) matrices with \(p\) columns)
for testing data,
and beta for effects (\(p \times q\) matrix).
Examples
data <- sim.data.transfer()
sapply(X=data$y_train,FUN=length)
#> [1] 50 100 200
sapply(X=data$X_train,FUN=dim)
#> [,1] [,2] [,3]
#> [1,] 50 100 200
#> [2,] 200 200 200
sapply(X=data$y_test,FUN=length)
#> [1] 10000 10000 10000
sapply(X=data$X_test,FUN=dim)
#> [,1] [,2] [,3]
#> [1,] 10000 10000 10000
#> [2,] 200 200 200
dim(data$beta)
#> [1] 200 3