inst/tinytest/gen_data_grp.R

In pbreheny/hdrm: High-Dimensional Regression Modeling

if (interactive()) library(tinytest)

Data <- gen_data_grp(100, 10, 5, J1=3, K1=2)
expect_equal(dim(Data$X), c(100, 50))
head(Data$y)
B <- matrix(Data$beta, ncol=10)
expect_false(any(B[1:2, 1:3]==0))
expect_true(all(B[3:5, 1:3]==0))
expect_true(all(B[, 4:10]==0))
expect_equal(Data$group, rep(1:10, each=5))

gen_data_grp(100, 3, 3, J1=2, K1=2)$beta
gen_data_grp(100, 3, 3, J1=2, K1=2, SNR=2)$beta
gen_data_grp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8)$beta
gen_data_grp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8, signal='het')$beta
gen_data_grp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8, signal='het', signal.g='het')$beta
gen_data_grp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8, signal='het', b=1)$beta

gen_data_grp(1000, 3, 3, rho=0)$X |> cor() |> round(digits=2)
gen_data_grp(1000, 3, 3, rho=0.7)$X |> cor() |> round(digits=2)
gen_data_grp(1000, 3, 3, rho=0.3, rho.g=0.8)$X |> cor() |> round(digits=2)
gen_data_grp(1000, 3, 3, rho=0.1, rho.g=0.5)$X |> cor() |> round(digits=2)

# timing check
n <- 100
J <- 1000
K <- 3
rho <- 0.3
rho.g <- 0.6
res <- bench::mark(
  genDataGrp(n, J, K, rho=rho, rho.g = rho.g),
  gen_data_grp(n, J, K, rho=rho, rho.g = rho.g),
  check=FALSE)
summary(res)
summary(res, relative = TRUE)
ggplot2::autoplot(res)