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PLNmodels (version 0.11.7)

Poisson Lognormal Models

Description

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 ) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

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Version

Install

install.packages('PLNmodels')

Monthly Downloads

355

Version

0.11.7

License

GPL (>= 3)

Maintainer

Julien Chiquet

Last Published

August 25th, 2022

Functions in PLNmodels (0.11.7)

An R6 Class to represent a collection of PLNPCAfit

An R6 Class to represent a collection of PLNfit

Poisson lognormal model

Poisson lognormal model towards Linear Discriminant Analysis

PLNmixturefamily

An R6 Class to represent a collection of PLNmixturefit

An R6 Class to represent a PLNfit in a standard, general framework

Poisson lognormal model towards Principal Component Analysis

Poisson lognormal mixture model

An R6 Class to represent a PLNfit in a LDA framework

An R6 Class to represent a PLNfit in a PCA framework

Extract model coefficients

coef.PLNmixturefit

Extract model coefficients

Compute offsets from a count data using one of several normalization schemes

An R6 Class to represent a PLNfit in a sparse inverse covariance framework

coefficient_path

Extract the regularization path of a PLNnetwork fit

An R6 Class to represent a PLNfit in a mixture framework

PLNnetworkfamily

An R6 Class to represent a collection of PLNnetworkfit

Extracts model coefficients from objects returned by PLNLDA()

Poisson lognormal model towards sparse network inference

Oaks amplicon data set

LDA visualization (individual and/or variable factor map(s)) for a PLNPCAfit object

fitted.PLNmixturefit

Extracts model fitted values from objects returned by PLNmixture() and its variants

Extract edge selection frequency in bootstrap subsamples

Extracts model fitted values from objects returned by PLN() and its variants

Mollusk data set

getBestModel.PLNPCAfamily

Best model extraction from a collection of models

getModel.PLNPCAfamily

Model extraction from a collection of models

plot.PLNPCAfamily

Display the criteria associated with a collection of PLNPCA fits (a PLNPCAfamily)

Display the criteria associated with a collection of PLN fits (a PLNfamily)

PCA visualization (individual and/or variable factor map(s)) for a PLNPCAfit object

Fisher information matrix for Theta

plot.PLNmixturefit

Mixture visualization of a PLNmixturefit object

plot.PLNmixturefamily

Display the criteria associated with a collection of PLNmixture fits (a PLNmixturefamily)

predict.PLNmixturefit

Prediction for a PLNmixturefit object

plot.PLNnetworkfamily

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of PLNnetwork fits (a PLNnetworkfamily)

stability_selection

Compute the stability path by stability selection

Calculate Variance-Covariance Matrix for a fitted PLN() model object

Predict counts conditionally

plot.PLNnetworkfit

Extract and plot the network (partial correlation, support or inverse covariance) from a PLNnetworkfit object

Predict counts of a new sample

predict.PLNLDAfit

Predict group of new samples

Component-wise standard errors of Theta

Extract variance-covariance of residuals 'Sigma'

Trichoptera data set

Prepare data for use in PLN models

sigma.PLNmixturefit

Extract variance-covariance of residuals 'Sigma'