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Merge pull request #56 from ocbe-uio/issue-54
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Added argument validation to MADMMplasso()
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Theo-qua authored Sep 2, 2024
2 parents a3ac0c4 + 195122c commit 9aa99e3
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: MADMMplasso
Title: Multi Variate Multi Response 'ADMM' with Interaction Effects
Version: 0.0.0.9022
Version: 0.0.0.9023
Authors@R:
c(
person(
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3 changes: 3 additions & 0 deletions R/MADMMplasso.R
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#' @example inst/examples/MADMMplasso_example.R
#' @export
MADMMplasso <- function(X, Z, y, alpha, my_lambda = NULL, lambda_min = 0.001, max_it = 50000, e.abs = 1E-3, e.rel = 1E-3, maxgrid, nlambda, rho = 5, my_print = FALSE, alph = 1.8, tree, pal = cl == 1L, gg = NULL, tol = 1E-4, cl = 1L, legacy = FALSE) {
# Validation
stopifnot(nlambda <= maxgrid)

# Recalculating the number of CPUs
if (pal && cl > 1L) {
cl <- 1L
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80 changes: 80 additions & 0 deletions tests/testthat/test-validation.R
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# Setting up objects =========================================================
N <- 100
p <- 50
nz <- 4
K <- nz
X <- matrix(rnorm(n = N * p), nrow = N, ncol = p)
mx <- colMeans(X)
sx <- sqrt(apply(X, 2, var))
X <- scale(X, mx, sx)
X <- matrix(as.numeric(X), N, p)
Z <- matrix(rnorm(N * nz), N, nz)
mz <- colMeans(Z)
sz <- sqrt(apply(Z, 2, var))
Z <- scale(Z, mz, sz)
beta_1 <- rep(x = 0, times = p)
beta_2 <- rep(x = 0, times = p)
beta_3 <- rep(x = 0, times = p)
beta_4 <- rep(x = 0, times = p)
beta_5 <- rep(x = 0, times = p)
beta_6 <- rep(x = 0, times = p)

beta_1[1:5] <- c(2, 2, 2, 2, 2)
beta_2[1:5] <- c(2, 2, 2, 2, 2)
beta_3[6:10] <- c(2, 2, 2, -2, -2)
beta_4[6:10] <- c(2, 2, 2, -2, -2)
beta_5[11:15] <- c(-2, -2, -2, -2, -2)
beta_6[11:15] <- c(-2, -2, -2, -2, -2)

Beta <- cbind(beta_1, beta_2, beta_3, beta_4, beta_5, beta_6)
colnames(Beta) <- 1:6

theta <- array(0, c(p, K, 6))
theta[1, 1, 1] <- 2
theta[3, 2, 1] <- 2
theta[4, 3, 1] <- -2
theta[5, 4, 1] <- -2
theta[1, 1, 2] <- 2
theta[3, 2, 2] <- 2
theta[4, 3, 2] <- -2
theta[5, 4, 2] <- -2
theta[6, 1, 3] <- 2
theta[8, 2, 3] <- 2
theta[9, 3, 3] <- -2
theta[10, 4, 3] <- -2
theta[6, 1, 4] <- 2
theta[8, 2, 4] <- 2
theta[9, 3, 4] <- -2
theta[10, 4, 4] <- -2
theta[11, 1, 5] <- 2
theta[13, 2, 5] <- 2
theta[14, 3, 5] <- -2
theta[15, 4, 5] <- -2
theta[11, 1, 6] <- 2
theta[13, 2, 6] <- 2
theta[14, 3, 6] <- -2
theta[15, 4, 6] <- -2

pliable <- matrix(0, N, 6)
for (e in 1:6) {
pliable[, e] <- compute_pliable(X, Z, theta[, , e])
}

esd <- diag(6)
e <- MASS::mvrnorm(N, mu = rep(0, 6), Sigma = esd)
y_train <- X %*% Beta + pliable + e
y <- y_train

colnames(y) <- c(paste0("y", seq_len(ncol(y))))
TT <- tree_parms(y)
gg1 <- matrix(0, 2, 2)
gg1[1, ] <- c(0.02, 0.02)
gg1[2, ] <- c(0.02, 0.02)

# Running MADMMplasso ========================================================
test_that("MADMMplasso() prevents silly arguments", {
expect_error(
MADMMplasso(X, Z, y, 0.2, maxgrid = 1, nlambda = 2, tree = TT, gg = gg1)
)
})

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