Added test for #53

tests/testthat/test-issue_53.R

@@ -0,0 +1,102 @@
+# Install and load the package, set the seed
+remotes::install_github("ocbe-uio/MADMMplasso")
+library(MADMMplasso)
+set.seed(1235)
+
+# Generate the data
+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) <- c(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
+
+library(MASS)
+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(paste("y", 1:(ncol(y)), sep = ""))
+TT <- tree_parms(y)
+
+gg1 <- matrix(0, 2, 2)
+gg1[1, ] <- c(0.02, 0.02)
+gg1[2, ] <- c(0.2, 0.2)
+nlambda <- 50
+e.abs <- 1E-4
+e.rel <- 1E-2
+alpha <- .5
+tol <- 1E-3
+
+# Fitting models
+message("fit_C")
+fit_C <- MADMMplasso(
+  X, Z, y,
+  alpha = alpha, my_lambda = NULL,
+  lambda_min = 0.001, max_it = 5000, e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda,
+  nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE, alph = 1,
+  pal = FALSE, gg = gg1, tol = tol, legacy = FALSE, cl = 3L
+)
+
+message("fit_R")
+fit_R <- MADMMplasso(
+  X, Z, y,
+  alpha = alpha, my_lambda = NULL,
+  lambda_min = 0.001, max_it = 5000, e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda,
+  nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE, alph = 1,
+  pal = FALSE, gg = gg1, tol = tol, legacy = TRUE, cl = 3L
+)