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lori

Package for the analysis of low-rank interaction contingency tables

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Install

install.packages('lori')

Monthly Downloads

669

Version

2.2.2

License

GPL-3

Maintainer

Genevieve Robin

Last Published

December 16th, 2020

Functions in lori (2.2.2)

The mi.lori performs M multiple imputations using the lori method. Multiple imputation allows to produce estimates of missing values, as well as intervals of variability. The classical procedure is to perform M multiple imputations using the mi.lori method, and to aggregate them using the pool.lori method.

automatic selection of nuclear norm regularization parameter

Alpine plant communities in Aravo, France: Abundance data and covariates

The cv.lori method performs automatic selection of the regularization parameters (lambda1 and lambda2) used in the lori function. These parameters are selected by cross-validation. The classical procedure is to apply cv.lori to the data to select the regularization parameters, and to then impute and analyze the data using the lori function (or mi.lori for multiple imputation).

The lori method implements a method to analyze and impute incomplete count tables. An important feature of the method is that it can take into account main effects of rows and columns, as well as effects of continuous or categorical covariates, and interaction. The estimation procedure is based on minimizing a Poisson loss penalized by a Lasso type penalty (sparse vector of covariate effects) and a nuclear norm penalty inducing a low-rank interaction matrix (a few latent factors summarize the interactions).

The pool.lori method aggregates lori multiple imputation results. Multiple imputation allows to produce estimates of missing values, as well as intervals of variability. The classical procedure is to perform multiple imputation using the mi.lori method, and to aggregate them using the pool.lori method.