Data simulation for related problems — sim_data

Simulates data for multi-task learning and transfer learning.

Usage

sim_data_multi(
  prob.common = 0.05,
  prob.separate = 0.05,
  q = 3,
  n0 = 100,
  n1 = 10000,
  p = 200,
  rho = 0.5,
  family = "gaussian"
)

sim_data_trans(
  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

Multi-task learning: 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).
Transfer learning: 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

#--- multi-task learning ---
data <- sim_data_multi()
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

#--- transfer learning ---
data <- sim_data_trans()
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