## 8 subjects, 8 time-points, 3 variables
inputData <- acuteInflammation$data[,1:3]
ind <- acuteInflammation$meta$ind
time <- acuteInflammation$meta$time
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 PC4 PC5 PC6
# R2 0.8924 0.0848 0.01055 0.006084 0.0038 0.002362
# Cumulative R2 0.8924 0.9772 0.98775 0.993838 0.9976 1.000000
get_eigen_DF(eigen)
# $df
# CV GCV AIC BIC AICc
# 3.362581 4.255487 3.031260 2.919159 2.172547
# $wdf
# CV GCV AIC BIC AICc
# 2.293130 2.085212 6.675608 6.671545 4.467724
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