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
- 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 a list with slots y_train and X_train for training data, y_test and X_test for testing data, and beta for effects. The training data contains vectors of different lengths (y_train) and matrices with different number of rows (X_train).
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