Last updated: 2022-10-21
Checks: 1 1
Knit directory: lglasso_data_analysis/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 520f495 | Jie Zhou | 2022-10-21 | updated code for all the figures and tables |
| html | 520f495 | Jie Zhou | 2022-10-21 | updated code for all the figures and tables |
| Rmd | e045a4e | Jie Zhou | 2022-09-29 | complete version |
| html | e045a4e | Jie Zhou | 2022-09-29 | complete version |
| Rmd | dd32d09 | Jie Zhou | 2022-09-28 | create the repo |
| html | dd32d09 | Jie Zhou | 2022-09-28 | create the repo |
| html | 4d8e172 | Jie Zhou | 2022-09-27 | Build site. |
| Rmd | 716a0c1 | Jie Zhou | 2022-09-27 | Start workflowr project. |
This website demonstrate the specific procedures to reproduce the results in the paper Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data. If you are only interested in how to use the R package lglasso associated with this paper rather than the results in the paper, you could just check the vignette by click the link, R package lglasso.
In the paper, we compared the proposed network identification algorithm lglasso with other conventional algorithms, i.e., glasso, neighborhood method, GGMselect-CO1 and GGMselect-LA. It is shown that the proposed lglasso outperform the other methods when the data are longitudinal. In order to carry out the simulation studies, in addition to the functions defined in package lglasso, we also defined some other functions to facilitate the simulation. These functions are then sourced into the simulation scripts.
Unfortunately, because the authors have not been authorized to disclose the real data used in the paper, we only demonstrate the procedure used to reproduce the results in simulation studies in the paper.
In order to run the script, you need to install the package first, using the following code,
remotes::install_github("jiezhou-2/lglasso",ref = "conditional")Note since in each figure, there are four scenarios being investigated which only differ in terms of their parameter settings, so only the code for one of the four scenarios are displayed. You can change the parameter setting to get the results for other settings. The same rule is used for the results in the tables. Also since the running code can take hours,if possible, I would suggest you to submit the code to a server instead of on your local computer.