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Code for Schnell, Tang, Offen & Carlin, "A Bayesian credible subgroups approach to identifying subgroups with positive treatment effects." Biometrics (2016)
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README.txt | ||
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alzheimers.csv | ||
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bayes-linear.R | ||
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decomp.R | ||
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example.R | ||
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simulate.R | ||
README.txt
FILES:
- alzheimers.csv: Contains cleaned data for the Alzheimer's
disease example.
- bayes-linear.R: Defines functions related to the methods
described in the paper.
- decomp.R Produces estimate, standard error, and observation
plots used in example analysis.
- example.R: Reproduces the Alzheimer's disease example analysis.
- simulate.R: Reproduces the simulation study.
TO RUN ALZHEIMER'S DISEASE ANALYSIS:
- Packages required: MASS, Matrix, matrixcalc, matrixStats, mvtnorm,
plotrix, vcd.
- Make sure working directory is set to the directory
containing the R scripts.
- Run example.R.
- To reproduce the decomposition plots, run decomp.R.
TO RUN SIMULATION STUDY:
- Packages required: MASS, Matrix, matrixcalc, matrixStats, mvtnorm,
BayesTree.
- Make sure working directory is set to the directory
containing the R scripts.
- Run simulate.R. This may take several hours.
- Simulation results are saved in tables-n.RData, where
n is the sample size (default 40).
TO ANALYZE A NEW DATASET:
- Load bayes-linear.R
- Defaults: call blm() with syntax used by lm().
- Custom prior: call blm(..., design=TRUE) to get the design
matrix, and specify hyperparameters accordingly.
- Create grid approximating the covariate space of interest.
- Call the find.credible.subgroups function.
- See example.R for an example use.