# \donttest{
# set seed
set.seed(236)
# create data and setup
data = matrix(rnorm(2000), ncol = 4)
groups = sample(1:10, 500, replace = TRUE)
W = time_weights(N = 10, c(3,2,1))
# calculate covariances
covs = ssMRCD(data, groups = groups, weights = W, lambda = 0.3)
# calculate sparse PCA
pca = sparsePCAloc(eta = 0.3, gamma = 0.7, cor = FALSE, COVS = covs$MRCDcov,
n_max = 1000, increase_rho = list(TRUE, 50, 1), trace = FALSE)
# align loadings
pca$PC = align_PC(PC = pca$PC, N = pca$N, p = pca$p, type = "mean")
# plot different PCA plots
plot(x = pca, type = "score_distances", groups = groups, X = data, ssMRCD = covs, k = 2)
plot(x = pca, type = "biplot", color = "variable")
plot(x = pca, type = "scores", groups = groups, X = data, ssMRCD = covs, k = 1)
plot(x = pca, type = "screeplot")
plot(x = pca, type = "loadings", k = 1)
# }
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