cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numer...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2023-01-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4335 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846101723103363072 |
|---|---|
| author | Luigi Augugliaro Gianluca Sottile Ernst C. Wit Veronica Vinciotti |
| author_facet | Luigi Augugliaro Gianluca Sottile Ernst C. Wit Veronica Vinciotti |
| author_sort | Luigi Augugliaro |
| collection | DOAJ |
| description |
Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1 -penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data.
|
| format | Article |
| id | doaj-art-c0828f7e1f33479d95762d33da3f28b9 |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-c0828f7e1f33479d95762d33da3f28b92024-12-29T00:12:54ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602023-01-01105110.18637/jss.v105.i014102cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing ValuesLuigi Augugliaro0Gianluca Sottile1Ernst C. Wit2Veronica Vinciotti3University of PalermoUniversity of PalermoUniversità della Svizzera italianaUniversity of Trento Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1 -penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data. https://www.jstatsoft.org/index.php/jss/article/view/4335conditional Gaussian graphical modelsglassohigh-dimensionalitysparsitycensoringmissing data |
| spellingShingle | Luigi Augugliaro Gianluca Sottile Ernst C. Wit Veronica Vinciotti cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values Journal of Statistical Software conditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing data |
| title | cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values |
| title_full | cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values |
| title_fullStr | cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values |
| title_full_unstemmed | cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values |
| title_short | cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values |
| title_sort | cglasso an r package for conditional graphical lasso inference with censored and missing values |
| topic | conditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing data |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4335 |
| work_keys_str_mv | AT luigiaugugliaro cglassoanrpackageforconditionalgraphicallassoinferencewithcensoredandmissingvalues AT gianlucasottile cglassoanrpackageforconditionalgraphicallassoinferencewithcensoredandmissingvalues AT ernstcwit cglassoanrpackageforconditionalgraphicallassoinferencewithcensoredandmissingvalues AT veronicavinciotti cglassoanrpackageforconditionalgraphicallassoinferencewithcensoredandmissingvalues |