## 8 subjects, 4 time-points, 3 variables
inputData <- acuteInflammation$data[0:32,1:3]
ind <- acuteInflammation$meta$ind[0:32]
time <- acuteInflammation$meta$time[0:32]
eigen <- get_eigen_spline(inputData, ind, time, nPC=NA, scaling="scaling_UV",
method="nipals", verbose=TRUE, centering=TRUE, ncores=0)
# nipals calculated PCA
# Importance of component(s):
# PC1 PC2 PC3
# R2 0.9272 0.06606 0.006756
# Cumulative R2 0.9272 0.99324 1.000000
# total time: 0.02 secs
get_param_evolution(eigen, step=1)
# [[1]]
# 2 3 4
# Penalised_residuals(CV) 103.55727 141.55548 267.197267
# Penalised_residuals(GCV) 90.84612 122.03917 198.953021
# AIC 185.57835 67.02707 8.000000
# BIC 184.35094 65.18611 5.545177
# AICc 197.57835 95464.81688 -32.000000
#
# [[2]]
# 2 3 4
# Penalised_residuals(CV) 0.2257652 6.401150e-01 1.512174
# Penalised_residuals(GCV) 0.3034771 6.647154e-01 1.173309
# AIC 4.6062841 6.331849e+00 8.000000
# BIC 3.3788728 4.490887e+00 5.545177
# AICc 16.6062865 9.540412e+04 -32.000000
#
# [[3]]
# 2 3 4
# Penalised_residuals(CV) 0.8338811 9.171538e-01 1.484069
# Penalised_residuals(GCV) 0.6607046 7.148925e-01 1.105211
# AIC 5.3094592 6.354912e+00 8.000000
# BIC 4.0820479 4.513949e+00 5.545177
# AICc 17.3094616 9.540414e+04 -32.000000
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