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, and can be reproduced easily via targets syntax.

How to run

1. Install the necessary workflow packages targets and renv 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. The simulation computation was conducted separately on a high-performance super computer, and hence not included in the current workflow. You can find the code to replicate the simulation process in the folder 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.

• The two datasets used for real data analyses are included in the repo for reproducibility purpose (placed in the folder Real_Data). The owner of this repository does not own the datasets, and suggest readers to check out the datasets on Dryad and Zendo

  • Emory Cardiovascular Biobank: https://datadryad.org/stash/dataset/doi:10.5061/dryad.866t1g1mt
  • Weight Loss Maintenance Cohort: https://zenodo.org/record/4767969

• 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()