I understand that at different parts of the timeOmics pipeline (e.g. checking optimal number of clusters using silhouette coefficient or checking distances within/between clusters), but I was wondering are there any additional ways to estimate uncertainty around the predictions that time Omics makes? For example, (if possible) using a resampling method like bootstrapping for the linear mixed model splines and getting confidence intervals or something else that might be more statistically valid. Any thoughts would be appreciated. Thank you
The lmms package is not maintained anymore, so if you want to have a look, it is better to use the cloud RStudio and choose the appropriate R version to load it). The main output of the lmms will give an idea of the type of model that was applied, but I don’t think it gives much more (to be checked).