Perform Logistic Normal Multinomial Clustering for Microbiome Compositional Data
Gives default initial guesses for logistic-normal multinomial bicluste...
Gives default initial guesses for penalized logistic-normal multinomia...
Gives default initial guesses for logistic-normal multinomial Factor a...
Logistic Normal Multinomial Biclustering algorithm
Logistic Normal Multinomial factor analyzer algorithm
run main microbiome bicluster algorithm.
Penalized Logistic Normal Multinomial factor analyzer main estimation ...
run main microbiome Factor Analyzer algorithm.
Model selections for lnmbicluster
Model selections for plnmfa
Model selections for lnmfa
Penalized Logistic Normal Multinomial factor analyzer algorithm
An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.