sim_data_multi {sparselink}R Documentation

Data simulation for related problems

Description

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

Examples

#--- multi-task learning ---
data <- sim_data_multi()
sapply(X=data,FUN=dim)

#--- transfer learning ---
data <- sim_data_trans()
sapply(X=data$y_train,FUN=length)
sapply(X=data$X_train,FUN=dim)
sapply(X=data$y_test,FUN=length)
sapply(X=data$X_test,FUN=dim)
dim(data$beta)


[Package sparselink version 1.0.0 Index]