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README from MADMMplasso
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# Install and load the package, set the seed | |
remotes::install_github("ocbe-uio/MADMMplasso@issue-17") | |
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_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, parallel = FALSE, | |
pal = TRUE, gg = gg1, tol = tol, legacy = TRUE | |
) | |
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, parallel = FALSE, | |
pal = TRUE, gg = gg1, tol = tol, legacy = FALSE | |
) |
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