Package: lori 2.2.2

Genevieve Robin

lori: Imputation of High-Dimensional Count Data using Side Information

Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <arxiv:1703.02296v4>.

Authors:Genevieve Robin [aut, cre]

lori_2.2.2.tar.gz
lori_2.2.2.tar.gz(r-4.5-noble)lori_2.2.2.tar.gz(r-4.4-noble)
lori_2.2.2.tgz(r-4.4-emscripten)lori_2.2.2.tgz(r-4.3-emscripten)
lori.pdf |lori.html✨
lori/json (API)

# Install 'lori' in R:
install.packages('lori', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • aravo - Alpine plant communities in Aravo, France: Abundance data and covariates

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.34 score 22 scripts 342 downloads 6 exports 8 dependencies

Last updated 4 years agofrom:43119a3086. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-linuxNOTENov 06 2024

Exports:covmatcv.lorilorimi.loripool.loriqut

Dependencies:data.tablelatticeMatrixrARPACKRcppRcppEigenRSpectrasvd

Aravo data analysis

Rendered fromaravo_data_analysis.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2020-12-16
Started: 2020-12-16

Getting started

Rendered fromgetting_started.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2020-12-16
Started: 2020-12-16

Readme and manuals

Help Manual

Help pageTopics
Alpine plant communities in Aravo, France: Abundance data and covariatesaravo
covmatcovmat
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).cv.lori
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).lori
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.mi.lori
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.pool.lori
automatic selection of nuclear norm regularization parameterqut