Multivariate Functional Additive Mixed Models
Compute the Number of FPCs needed
Conduct the MFPCA
Extract Model Components to be Compared
Extract Model Components to be Compared from Univariate Model
Extract Variance Information from MFPCA Object
Multivariate Functional Additive Mixed Model Regression
Predict The Mean Function For the FPC Plots
Prepare Information Necessary for MFPCA
Prune the MFPC object to include only a prespecified level of explaine...
Refit the model under an independence assumption
An implementation for multivariate functional additive mixed models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse functional regression models and multivariate functional principal component analysis. This package contains the function to run a multiFAMM and some convenience functions useful when working with large models. An additional package on GitHub contains more convenience functions to reproduce the analyses of the corresponding paper (<https://github.com/alexvolkmann/multifammPaper>).