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.
PLNmodels needs the following CRAN R packages, so check that they are are installed on your computer.
required_CRAN <- c("R6", "glassoFast", "Matrix", "Rcpp", "RcppArmadillo", "nloptr", "igraph", "grid", "gridExtra", "dplyr", "tidyr", "ggplot2", "corrplot", "magrittr", "devtools") not_installed_CRAN <- setdiff(required_CRAN, rownames(installed.packages())) if (length(not_installed_CRAN) > 0) install.packages(not_installed_CRAN)
PLNmodels also needs two BioConductor packages
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 http://jchiquet.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)
Please cite our work using the following references:
J. Chiquet, M. Mariadassou and S. Robin: Variational inference for probabilistic Poisson PCA, the Annals of Applied Statistics, 12: 2674–2698, 2018. 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