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PLNmodels (version 0.9.5)

Poisson Lognormal Models

Description

The Poisson-lognormal model and variants can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data (Chiquet, Mariadassou and Robin, 2018 ), discriminant analysis and network inference (Chiquet, Mariadassou and Robin, 2018 ). 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

0.9.5

License

GPL (>= 3)

Maintainer

Julien Chiquet

Last Published

January 27th, 2020

Functions in PLNmodels (0.9.5)

PLNLDAfit

An R6 Class to represent a PLNfit in a LDA framework
PLNPCAfamily

An R6 Class to represent a collection of PLNPCAfit
PLNfamily

An R6 Class to represent a collection of PLNfit
PLNmodels

PLNmodels
PLNnetwork

Poisson lognormal model towards sparse network inference
PLNPCAfit

An R6 Class to represent a PLNfit in a PCA framework
PLNPCA

Poisson lognormal model towards Principal Component Analysis
PLN

Poisson lognormal model
PLNfit

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

Poisson lognormal model towards Linear Disciminant Analysis
PLNnetworkfamily

An R6 Class to represent a collection of PLNnetworkfit
compute_offset

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

Pipe operator
prepare_data

Prepare data for use in PLN models
rPLN

PLN RNG
fisher

Fisher information matrix for Theta
plot.PLNLDAfit

LDA vizualiation (individual and/or variable factor map(s)) for a PLNPCAfit object
plot.PLNPCAfamily

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

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

Extracts model fitted values from objects returned by PLN and its variants
plot.PLNPCAfit

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

Extract the regularization path of a PLNnetwork fit
getBestModel.PLNPCAfamily

Best model extraction from a collection of models
predict.PLNLDAfit

Predict group of new samples
standard_error

Component-wise standard errors of Theta
coef.PLNfit

Extract model coefficients
coef.PLNLDAfit

Extracts model coefficients from objects returned by PLNLDA
getModel.PLNPCAfamily

Model extraction from a collection of models
extract_probs

Extract edge selection frequency in bootstrap subsamples
mollusk

Mollusk data set
predict.PLNfit

Predict counts of a new sample
trichoptera

Trichoptera data set
vcov.PLNfit

Calculate Variance-Covariance Matrix for a fitted PLN model object
plot.PLNnetworkfamily

Display various ouputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of PLNnetwork fits (a PLNnetworkfamily)
plot.PLNnetworkfit

Extract and plot the network (aprtial correlation, support or inverse covariance) from a PLNnetworkfit object
sigma.PLNfit

Extract variance-covariance of residuals 'Sigma'
stability_selection

Compute the stability path by stability selection