The Poisson lognormal model and variants can be used for a variety of multivariate problems when count data are at play (including PCA, LDA and network inference for count data). This package implements efficient algorithms to fit such models accompanied with a set of functions for visualization and diagnostic. See this deck of slides for a comprehensive introduction.
The package comes with an ecological data set to present the functionality
library(PLNmodels) data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
The main fitting functions work with the usual
R formula notations, with mutivariate responses on the left hand side. You probably want to start by one of them. Check the corresponding vignette and documentation page. There is a dedicated vignettes for each model in the package (See https://pln-team.github.io/PLNmodels/articles/).
myPLN <- PLN(Abundance ~ 1, data = trichoptera)
myPCA <- PLNPCA(Abundance ~ 1, data = trichoptera, ranks = 1:8)
myLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)
myPLNnetwork <- PLNnetwork(Abundance ~ 1, data = trichoptera)
myPLNmixture <- PLNmixture(Abundance ~ 1, data = trichoptera)
Please cite our work using the following references:
J. Chiquet, M. Mariadassou and S. Robin: The Poisson-lognormal model as a versatile framework for the joint analysis of species abundances, bioRxiv preprint, 2020.. link
J. Chiquet, M. Mariadassou and S. Robin: Variational inference for sparse network reconstruction from count data, Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. link
J. Chiquet, M. Mariadassou and S. Robin: Variational inference for probabilistic Poisson PCA, the Annals of Applied Statistics, 12: 2674–2698, 2018. link