prune_mfpc: Prune the MFPC object to include only a prespecified level of...
In multifamm: Multivariate Functional Additive Mixed Models

This is an internal function contained in the multiFAMM function. This function takes the MFPCA object and decides how many functional principal components are to be included in the model.

1 2	prune_mfpc(MFPC, mfpc_cutoff, model_list, mfpc_cut_method, number_mfpc, mfpca_info)

`MFPC`	List containing MFPC objects for each variance component as given by the function conduct_mfpca()
`mfpc_cutoff`	Pre-specified level of explained variance of results of MFPCA. Defaults to 0.95.
`model_list`	List containing sparseFLMM objects for each dimension as given by the output of apply_sparseFLMM()
`mfpc_cut_method`	Method to determine the level of explained variance total_var: (weighted) sum of variation over the dimensions. unidim: separate on each dimension.
`number_mfpc`	List containing the number of mfPCs needed for each variance component e.g. list("E" = x, "B" = y).
`mfpca_info`	Object containing all the neccessary information for the MFPCA. List as given by the output of prepare_mfpca().