- usage: /home/afniHQ/afni.build/pub.dist/bin/linux_ubuntu_16_64/BayesianGroupAna.py
[-h] -dataTable DATATABLE -y VAR [-help] [-prefix PREFIX] [-x VAR [VAR …]] [-no_center] [-iterations ITER] [-chains CHAINS] [-control_list LIST] [-plot] [-more_plots TYPE [TYPE …]] [-RData] [-seed SEED] [-overwrite]
This program conducts Bayesian Group Analysis (BGA) on a list (e.g., 6 or more) of regions of interest (ROIs) as laid out in Chen et al. (2018, https://www.biorxiv.org/content/early/2018/02/20/238998). Compared to the conventional univariate GLM in which each voxel or ROI is considered autonomous and analyzed independently, BGA pools and shares the information across the ROIs in a multilevel system. It is the probability of incorrect sign, instead of false positive rate that is controlled. In other words, there is only one BGA model that incorporates the data from all ROIs. This will explore the effect of X on Y at each ROI. The computation may take a few minutes or more depending on the amount of input data and model complexity. The final inferences are conducted through the posterior distribution or quantile intervals for each effect that are provided in a table in the output. A boxplot can also be generated if requested with -plot. The computation requires that the R package "brms" be installed (e.g., through rPkgsInstall). More info on the brms package can be found here: https://CRAN.R-project.org/package=brms And the brms reference manual is here: https://cran.r-project.org/web/packages/brms/brms.pdf
Similar to 3dMVM and 3dLME, a data table should be created containing the input data and relevant variables (with at least 3 columns: subject labels, ROI labels, response variable values). The -dataTable should be formated as follows: Subj ROI some_y some_x other_x S001 roi1 0.12 0.056 0.356 S001 roi2 0.65 0.232 0.231 S002 roi1 0.14 0.456 0.856 S002 roi2 0.64 0.432 0.431 The Subj and ROI columns must be included with the exact spelling!! If there are no x variables, only the intercept will be calculated.
Given -prefix is "gangBGA" and -x is "some_x", the default outputs are the following files: gangBGA_summary.txt: Summary of the brmsfit object from R. gangBGA_rhats.csv: rhats for each effect and x variable combination. gangBGA_Intercept_table.csv: Table with the MedianEst, StdDev, 2.50%, 5%, 50%, 95%, and 97.50% of each ROI for the Intercept term. gangBGA_some_x_table.csv: The same table as the Intercept but for the some_x variable.
All x variables are centered by default. The boxplot with -plot is not a standard boxplot. It is a plot of the 2.50%, 5%, 50%, 95%, 97.50% percentiles. The coloring of the boxes is determined by where the zero line crosses the box and whiskers. White: The zero line crosses the main box (between 5% and 95%). Purple: The zero line crosses between the whiskers and the main box. (2.50% to 5%) OR (95% to 97.50%) Red: The zero line does not cross the box or the whiskers. Additional plot types for -more_plots include (not sure all of these work): hist dens hist_by_chain dens_overlay violin intervalsareas acf acf_bar trace trace_highlight rhat rhat_hist neff neff_hist nuts_acceptance nuts_divergence nuts_stepsize nuts_treedepth nuts_energy Tables and plots will be created for the intercept and all specified x variables separately. So there may be a lot of output.
Minimum requirement only calculates the intercept (may not be useful). BayesianGroupAna.py -dataTable my_roi_data.txt -y zscore More useful. Calculates 2 x variables and saves out some plots. BayesianGroupAna.py -dataTable my_roi_data.txt \ -prefix dock_of_the_bayes \ -y zscore -x some_x other_x \ -chains 4 -iterations 1000 \ -plot -more_plots rhat violin
Required arguments: -dataTable DATATABLE Input text file. -y VAR Column name for the y variable. Optional arguments: -h, --help show this help message and exit -help Show this help. -prefix PREFIX Name of the output file. -x VAR [VAR ...] Column name for the x variables. If not specified, only the intercept will be added. -no_center Disable centering on the x variables. Maybe useful if you centered manually. -iterations ITER Number of total iterations per chain including warmup. Default  -chains CHAINS Number of Markov chains. Default  -control_list LIST Comma separated list of control parameters to pass to the brm function. (example: 'adapt_delta=0.99,max_treedepth=20'). Default is the brm function defaults -plot Output box, fit, and posterior prediction plots. -more_plots TYPE [TYPE ...] Output "stanplots" given different types of plot names. -RData Save the R session workspace and data. -seed SEED Seed to generate random number. Default  -overwrite Overwrites the output files. Justin Rajendra circa 05/2018 4 Gang Box... Keep on keeping on!