https://github.com/boyiguo1/manuscript-bham
Manuscript Repository of _Spike-and-Slab LASSO Generalized Additive Models and Scalable Algorithms for High-Dimensional Data Analysis_
https://github.com/boyiguo1/manuscript-bham
generalized-additive-models manuscript rstats spike-and-slab
Last synced: about 2 months ago
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Manuscript Repository of _Spike-and-Slab LASSO Generalized Additive Models and Scalable Algorithms for High-Dimensional Data Analysis_
- Host: GitHub
- URL: https://github.com/boyiguo1/manuscript-bham
- Owner: boyiguo1
- Created: 2021-05-03T15:09:47.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-05-16T02:40:24.000Z (about 3 years ago)
- Last Synced: 2025-03-15T02:46:46.329Z (2 months ago)
- Topics: generalized-additive-models, manuscript, rstats, spike-and-slab
- Language: TeX
- Homepage: https://onlinelibrary.wiley.com/doi/10.1002/sim.9483
- Size: 99.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Manuscript Repository
This is the repository for the manuscript *Spike-and-Slab Generalized Additive Models and Scalable Algorithms for High-Dimensional Data*. The repository is set up using the R workflow package [`targets`](https://cran.r-project.org/web/packages/targets/index.html), and can be reproduced easily via `targets` syntax.
## How to run
1. Install the necessary workflow packages [`targets`](https://cran.r-project.org/web/packages/targets/index.html) and [`renv`](https://rstudio.github.io/renv/articles/renv.html) if you don't already have
2. Open the R console and call `renv::restore()` to install the required R packages. Please give permission to install the necessary packages. This will mirror the version of packages used in the creation of the manuscript exactly.
3. call the `targets::tar_make()` function to run the pipeline; for example `targets::tar_make("manu")` to create the manuscript, and `targets::tar_make("manu_app")` to create the supporting information.
## Remarks
- The raw/unaggregated the simulation results can be accessed [here](https://www.dropbox.com/sh/9f8kvnduuqxwj7o/AADSULXkTpO5-DskQo0CaHMia?dl=0). The simulation computation was conducted separately on [a high-performance super computer](https://www.uab.edu/it/home/research-computing/cheaha), and hence not included in the current workflow. You can find the code to replicate the simulation process in the folder [`Simulation\Code`](https://github.com/boyiguo1/Manuscript-Spike_Slab_HD_GAM/tree/main/Simulation/Code) and the instruction to deploy the simulation on a cluster with the Slurm scheduling system via the repo [`boyiguo1/
Tutorial-Sim_Cluster_Composer`](https://github.com/boyiguo1/Tutorial-Sim_Cluster_Composer).
- The two datasets used for real data analyses are included in the repo for reproducibility purpose (placed in the folder [`Real_Data`](https://github.com/boyiguo1/Manuscript-Spike_Slab_HD_GAM/tree/main/Real_Data)). The owner of this repository does not own the datasets, and suggest readers to check out the datasets on [Dryad](https://datadryad.org/stash/) and [Zendo](https://zenodo.org/)
- Emory Cardiovascular Biobank:
- Weight Loss Maintenance Cohort:
- The `sparseGAM` package (v1.0.99) included in the `renv` environment differs from the current CRAN version (v1.0 as on Oct. 22, 2021). The difference being the cross-validation function `cv.SBGAM` will return prevalid response for out-of-sample statistics calculation.
- To root down the difference, check out `cv.SSGL.output` in the function `cv.SSGL`
- The modified version (v1.0.99) is located at `renv\local\sparseGAM_1.0.99.tar.gz`
- For Windows users with limited choices of compilers, there might be packages needs compilation from source, e.g. `xfun` package. In this case, I encourage you to install the binary version of the package locally and use `renv::hydrate()` before `renv::restore()`to cache the necessary packages from your library.
```
renv::hydrate()
renv::restore()
```