:orphan: .. _ahelp_TRR: *** TRR *** .. contents:: :local: | Welcome to TRR ============== .. code-block:: none Test-Retest Reliability Program through Bayesian Multilevel Modeling #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Version 0.0.5, March 13, 202r32 Author: Gang Chen (gangchen@mail.nih.gov) Website - https://afni.nimh.nih.gov/gangchen_homepage SSCC/NIMH, National Institutes of Health, Bethesda MD20892 #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Usage: ====== .. code-block:: none TRR performs test-rest reliability analysis for behavior data as well as region-based neuroimaging data. If no multiple trials are involved in a dataset, use the conventional intraclass correlation (ICC) with, for example, 3dICC for neuroimaging data. However, when there are multiple trials for each condition, the traditional intraclass correlation may underestimate TRR to various extent. 3dLMEr could be utilized with the option -TRR to estimate test-retest reliability with trial-level data for whole-brain analysis; however, it may only work for data with strong effects such as a single effect (e.g., one condition or average across conditions). The input data for the program TRR have to be at the trial level without any summarization at the condition level. The TRR estimation is conducted through a Byesian multilevel model with a shell script (as shown in the examples below). The input data should be formulated in a pure-text table that codes all the variables. Citation: ========= If you want to cite the modeling approach for TRR, consider the following +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. code-block:: none Chen G, et al., Beyond the intraclass correlation: A hierarchical modeling approach to test-retest assessment. https://www.biorxiv.org/content/10.1101/2021.01.04.425305v1 Read the following carefully! A data table in pure text format is needed as input for an TRR script. The data table should contain at least 3 (with a single condition) or 4 (with two conditions) columns that specify the information about subjects, sessions and response variable values: Subj session Y S1 T1 0.2643 S1 T2 0.3762 Subj condition session Y S1 happy T1 0.2643 S1 happy T2 0.3762 S1 sad T1 0.3211 S1 sad T2 0.3341 0) Through Bayesian analysis, a whole TRR distribution will be presented in the end as a density plot in PDF. In addition, the distribution is summarized with a mode (peak) and a highest density interval that are stored in a text file with a name specified through -prefix with the appendix .txt. 1) Avoid using pure numbers to code the labels for categorical variables. The column order does not matter. You can specify those column names as you prefer, but it saves a little bit scripting if you adopt the default naming for subjects ('Subj'), sessions ('sess') and response variable ('Y'). 2) Sampling error for the trial-level effects can be incorporated into the model. This is especially applicable to neuroimaging data where the trial level effects are typically estimated through time series regression with GLS (e.g., 3dREMLfit in AFNI); thus, the standard error or t-statistic can be provided as part of the input through an extra column in the data table and through the option -se in the TRR script. 3) If there are more than 4 CPUs available, one could take advantage of within chain parallelization through the option -WCP. However, extra stpes are required: both 'cmdstan' and 'cmdstanr' have to be installed. To install 'cmdstanr', execute the following command in R: install.packages('cmdstanr', repos = c('https://mc-stan.org/r-packages/', getOption('repos'))) Then install 'cmdstan' using the following command in R: cmdstanr::install_cmdstan(cores = 2) 4) The results from TRR can be slightly different from each execution or different computers and R package versions due to the nature of randomness involved in Monte Carlo simulations, but the differences should be negligle unless numerical failure occurs. Installation requirements: ========================== .. code-block:: none In addition to R installation, the R packages "brms", "coda" and "ggplot2" are required for TRR. Make sure you have a recent version of R. To install these packages, run the following command at the terminal: rPkgsInstall -pkgs "brms,coda,ggplot2" -site http://cran.us.r-project.org" Alternatively you may install them in R: install.packages("brms") install.packages("coda") install.packages("ggplot2") To take full advantage of parallelization, install both 'cmdstan' and 'cmdstanr' and use the option -WCP in TRR (see comments above). Running: ======== .. code-block:: none Once the TRR command script is constructed saved as a text file, for example, called myTRR.txt, execute it with the following (assuming on tcsh shell), nohup tcsh -x myTRR.txt > diary.txt & nohup tcsh -x myTRR.txt |& tee diary.txt & The progression of the analysis is stored in the text file diary.txt and can be examined later. The 'nohup' command allows the analysis running in the background even if the terminal is killed. Examples: ========= .. code-block:: none Example 1 --- TRR estimation for a single effect - simple scenario: one condition, two sessions. Trial level effects are the input from each subject, and test-retest reliability between two sessions is the research focus. TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -dataTable myData.tbl \ If a computer is equipped with as many CPUs as a factor 4 (e.g., 8, 16, 24, ...), a speedup feature can be adopted through within-chain parallelization with the option -WCP. For example, the script assumes a computer with 24 CPUs (6 CPUs per chain): TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -WCP 6 -dataTable myData.tbl \ If the data are skewed or have outliers, use exGaussian or Student's t: TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -distY exgaussian -dataTable myData.tbl \ TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -distY student -dataTable myData.tbl \ The input file 'myData.txt' is a data table in pure text format as below: Subj sess Y S01 sess1 0.162 S01 sess1 0.212 S01 sess2 -0.598 S01 sess2 0.327 S02 sess1 0.249 S02 sess1 0.568 Example 2 --- TRR estimation for a contrast between two conditions. Input data include trial-level effects for two conditions during two sessions. TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -dataTable myData.tbl \ A version with within-chain parallelization through option '-WCP 6' on a computer with 24 CPUs: TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -WCP 6 \ -dataTable myData.tbl \ Another version with the assumption of student t-distribution: TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -distY student -dataTable myData.tbl \ The input file 'myData.txt' is a data table in pure text format as below: Subj sess cond Y S01 sess1 C1 0.162 S01 sess1 C1 0.212 S01 sess1 C2 0.262 S01 sess1 C2 0.638 S01 sess2 C1 -0.598 S01 sess2 C1 0.327 S01 sess2 C2 0.249 S01 sess2 C2 0.568 Example 3 --- TRR estimation for a contrast between two conditions. Input data include trial-level effects plus their t-statistic or standard error values for two conditions during two sessions. TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -tstat tvalue -dataTable myData.tbl \ TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -se SE -dataTable myData.tbl \ A version with within-chain parallelization through option '-WCP 6' on a computer with 24 CPUs: TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -tstat tvalue -WCP 6 \ -dataTable myData.tbl \ Another version with the assumption of Student t-distribution: TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \ -repetition sess -condition cond -tstat tvalue -distY student \ -dataTable myData.tbl \ The input file 'myData.txt' is a data table in pure text format as below: Subj sess cond tvalue Y S01 sess1 C1 2.315 0.162 S01 sess1 C1 3.212 0.341 S01 sess1 C2 1.262 0.234 S01 sess1 C2 0.638 0.518 S01 sess2 C1 -2.598 -0.213 S01 sess2 C1 3.327 0.423 S01 sess2 C2 4.249 0.791 S01 sess2 C2 3.568 0.351 Options: ======== .. code-block:: none Options in alphabetical order: -chains N: Specify the number of Markov chains. Make sure there are enough processors available on the computer. Most of the time 4 cores are good enough. However, a larger number of chains (e.g., 8, 12) may help achieve higher accuracy for posterior distribution. Choose 1 for a single-processor computer, which is only practical only for simple models. -condition var_name: var_name is used to specify the column name that is designated as the condition variable. Currently TRR can only handle two conditions. Note that when this option is not invoked, no condition variable is assumed to be present, and the TRR analysis will proceed with a singl effect instead of a contrast between two conditions. -cVars variable_list: Identify categorical (qualitive) variables (or factors) with this option. The list with more than one variable has to be separated with comma (,) without any other characters such as spaces and should be surrounded within (single or double) quotes. For example, -cVars "sex,site" -dataTable TABLE: List the data structure in a table of long format (cf. wide format) in R with a header as the first line. NOTE: 1) There should have at least three columns in the table. These minimum three columns can be in any order but with fixed and reserved with labels: 'Subj', 'ROI', and 'Y'. The column 'ROI' is meant to code the regions that are associated with each value under the column Y. More columns can be added in the table for explanatory variables (e.g., groups, age, site) if applicable. Only subject-level (or between-subjects) explanatory variables are allowed at the moment. The labels for the columns of 'Subj' and 'ROI' can be any identifiable characters including numbers. 2) Each row is associated with one and only one 'Y' value, which is the response variable in the table of long format (cf. wide format) as defined in R. With n subjects and m regions, there should have totally mn rows, assuming no missing data. 3) It is fine to have variables (or columns) in the table that are not used in the current analysis. 4) The context of the table can be saved as a separate file, e.g., called table.txt. In the script specify the data with '-dataTable table.txt'. This option is useful when: (a) there are many rows in the table so that the program complains with an 'Arg list too long' error; (b) you want to try different models with the same dataset. -dbgArgs: This option will enable R to save the parameters in a file called .TRR.dbg.AFNI.args in the current directory so that debugging can be performed. -distY distr_name: Use this option to specify the distribution for the response variable. The default is Gaussian when this option is not invoked. When skewness or outliers occur in the data, consider adopting the Student's t-distribution, exGaussian, log-normal etc. by using this option with 'student', 'exgaussian', 'lognormal' and so on. -help: this help message -iterations N: Specify the number of iterations per Markov chain. Choose 1000 (default) for simple models (e.g., one or no explanatory variables). If convergence problem occurs as indicated by Rhat being great than 1.1, increase the number of iterations (e.g., 2000) for complex models, which will lengthen the runtime. Unfortunately there is no way to predict the optimum iterations ahead of time. -model FORMULA: This option specifies the effects associated with explanatory variables. By default (without user input) the model is specified as 1 (Intercept). Currently only between-subjects factors (e.g., sex, patients vs. controls) and quantitative variables (e.g., age) are allowed. When no between-subject factors are present, simply put 1 (default) for FORMULA. The expression FORMULA with more than one variable has to be surrounded within (single or double) quotes (e.g., '1+sex', '1+sex+age'. Variable names in the formula should be consistent with the ones used in the header of data table. A+B represents the additive effects of A and B, A:B is the interaction between A and B, and A*B = A+B+A:B. Subject as a variable should not occur in the model specification here. -PDP width height: Specify the layout of posterior distribution plot (PDP) with the size of the figure windown is specified through the two parameters of width and height in inches. -prefix PREFIX: Prefix is used to specify output file names. The main output is a text with prefix appended with .txt and stores inference information for effects of interest in a tabulated format depending on selected options. The prefix will also be used for other output files such as visualization plots, and saved R data in binary format. The .RData can be used for post hoc processing such as customized processing and plotting. Remove the .RData file to save disk space once you deem such a file is no longer useful. -qVars variable_list: Identify quantitative variables (or covariates) with this option. The list with more than one variable has to be separated with comma (,) without any other characters such as spaces and should be surrounded within (single or double) quotes. For example, -qVars "Age,IQ" -repetition var_name: var_name is used to specify the column name that is designated as for the repetition variable such as sess