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Code Supplement for "The Impact of Model Assumptions in Scalar-on-Image Regression"
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README.md

Code Supplement

This repository contains the code supplement for the paper

The Impact of Model Assumptions in Scalar-on-Image Regression

Clara Happ, Sonja Greven, Volker Schmid (Department of Statistics, LMU Munich, Munich, Germany)
for the Alzheimer's Disease Neuroimaging Initiative. Statistics in Medicine, 37(28): 4298-4317. The full article is available here.

It provides:

  • Usage examples for all models used in the paper
  • R implementations of methods, if not already available
  • R functions for all measures developed in the paper
  • ADNI roster IDs (RID) of the subjects used in the simulation settings (sample size 250 and 500) and in the application (sample size 754). We use slice z = 75 of each three-dimensional brain scan and select the coordinates x = 30:93, y = 30:93 to obtain the quadratic sub-images.
  • Code for generating the beta-images for the simulation, together with csv-files containing the final images (bumpy, pca, smooth, sparse)

Usage

R functions are directly applicable. The C implementation of the Bayesian GMRF models requires compilation. Change to the C subdirectory and run the following code in the command line (tested under Linux only)

  1. R CMD SHLIB utilities/*.c (compiles all utility functions)
  2. R CMD SHLIB mainGibbs_GMRF.c utilities/*.o (compiles main for GMRF)
  3. R CMD SHLIB mainGibbs_HyperparamsFixed.c utilities/*.o (compiles main for SparseGMRF)

Make sure that the Makevars file is in the same directory as the main files.

Bug reports

Please use GitHub issues for reporting bugs or issues.

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