# set seed
set.seed(236)
data = matrix(rnorm(2000), ncol = 4)
groups = sample(1:10, 500, replace = TRUE)
W = time_weights(N = 10, c(3,2,1))
# calculate covariance matrices
covs = ssMRCD(data, groups = groups, weights = W, lambda = 0.3)
# 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)
# plot score distances
plot_loadings(object = pca,
k = 1,
size = 2)
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