# R/dd_exp.R In hBayesDM: Hierarchical Bayesian Modeling of Decision-Making Tasks

#### Documented in dd_exp

#' Delay Discounting Task
#'
#' @description
#' Hierarchical Bayesian Modeling of the Delay Discounting Task using the following parameters: "r" (exponential discounting rate) & "beta" (inverse temp.).
#'
#' \strong{MODEL:}
#' Exponential Model (Samuelson, 1937, The Review of Economic Studies)
#'
#' @param data A .txt file containing the data to be modeled. Data columns should be labelled as follows: "subjID", "delay_later", "amount_later", "delay_sooner", "amount_sooner", and "choice". See \bold{Details} below for more information.
#' @param niter Number of iterations, including warm-up.
#' @param nwarmup Number of iterations used for warm-up only.
#' @param nchain Number of chains to be run.
#' @param ncore Integer value specifying how many CPUs to run the MCMC sampling on. Defaults to 1.
#' @param nthin Every \code{i == nthin} sample will be used to generate the posterior distribution. Defaults to 1. A higher number can be used when auto-correlation within the MCMC sampling is high.
#' @param inits Character value specifying how the initial values should be generated. Options are "fixed" or "random" or your own initial values.
#' @param indPars Character value specifying how to summarize individual parameters. Current options are: "mean", "median", or "mode".
#' @param saveDir Path to directory where .RData file of model output (\code{modelData}) can be saved. Leave blank if not interested.
#' @param modelRegressor Exporting model-based regressors? TRUE or FALSE. Currently not available for this model.
#' @param vb             Use variational inference to approximately draw from a posterior distribution. Defaults to FALSE.
#' @param inc_postpred Include trial-level posterior predictive simulations in model output (may greatly increase file size). Defaults to FALSE.
#' @param adapt_delta Floating point number representing the target acceptance probability of a new sample in the MCMC chain. Must be between 0 and 1. See \bold{Details} below.
#' @param stepsize Integer value specifying the size of each leapfrog step that the MCMC sampler can take on each new iteration. See \bold{Details} below.
#' @param max_treedepth Integer value specifying how many leapfrog steps that the MCMC sampler can take on each new iteration. See \bold{Details} below.
#'
#' @return \code{modelData} A class \code{"hBayesDM"} object with the following components:
#' \describe{
#'  \item{\code{model}}{Character string with the name of the model (\code{"dd_exp"}).}
#'  \item{\code{allIndPars}}{\code{"data.frame"} containing the summarized parameter
#'    values (as specified by \code{"indPars"}) for each subject.}
#'  \item{\code{parVals}}{A \code{"list"} where each element contains posterior samples
#'    over different model parameters. }
#'  \item{\code{fit}}{A class \code{"stanfit"} object containing the fitted model.}
#'  \item{\code{rawdata}}{\code{"data.frame"} containing the raw data used to fit the model, as specified by the user.}
#' }
#'
#' @importFrom rstan vb sampling stan_model rstan_options extract
#' @importFrom parallel detectCores
#' @importFrom stats median qnorm density
#'
#' @details
#' This section describes some of the function arguments in greater detail.
#'
#' \strong{data} should be assigned a character value specifying the full path and name of the file, including the file extension
#' (e.g. ".txt"), that contains the behavioral data of all subjects of interest for the current analysis.
#' The file should be a \strong{tab-delimited} text (.txt) file whose rows represent trial-by-trial observations and columns
#' represent variables. For the Delay Discounting Task, there should be six columns of data
#' with the labels "subjID", "delay_later", "amount_later", "delay_sooner", "amount_sooner", and "choice".
#' It is not necessary for the columns to be in this particular order, however it is necessary that they be labelled
#' correctly and contain the information below:
#' \describe{
#'  \item{\code{"subjID"}}{A unique identifier for each subject within data-set to be analyzed.}
#'  \item{\code{"delay_later"}}{An integer representing the delayed days for the later option within the given trial. (e.g., 1 6 15 28 85 170).}
#'  \item{\code{"amount_later"}}{A floating number representing the amount for the later option within the given trial. (e.g., 10.5 38.3 13.4 31.4 30.9, etc.).}
#'  \item{\code{"delay_sooner"}}{An integer representing the delayed days for the sooner option (e.g., 0 0 0 0).}
#'  \item{\code{"amount_sooner"}}{A floating number representing the amount for the sooner option (e.g., 10 10 10 10).}
#'  \item{\code{"choice"}}{An integer value representing the chosen option within the given trial (e.g., 0=instant amount, 1=delayed amount )}
#' }
#' \strong{*}Note: The data.txt file may contain other columns of data (e.g. "Reaction_Time", "trial_number", etc.), but only the data with the column
#' names listed above will be used for analysis/modeling. As long as the columns above are present and labelled correctly,
#' there is no need to remove other miscellaneous data columns.
#'
#' \strong{nwarmup} is a numerical value that specifies how many MCMC samples should not be stored upon the
#' beginning of each chain. For those familiar with Bayesian methods, this value is equivalent to a burn-in sample.
#' Due to the nature of MCMC sampling, initial values (where the sampling chain begins) can have a heavy influence
#' on the generated posterior distributions. The \code{nwarmup} argument can be set to a high number in order to curb the
#' effects that initial values have on the resulting posteriors.
#'
#' \strong{nchain} is a numerical value that specifies how many chains (i.e. independent sampling sequences) should be
#' used to draw samples from the posterior distribution. Since the posteriors are generated from a sampling
#' process, it is good practice to run multiple chains to ensure that a representative posterior is attained. When
#' sampling is completed, the multiple chains may be checked for convergence with the \code{plot(myModel, type = "trace")}
#' command. The chains should resemble a "furry caterpillar".
#'
#' \strong{nthin} is a numerical value that specifies the "skipping" behavior of the MCMC samples being chosen
#' to generate the posterior distributions. By default, \code{nthin} is equal to 1, hence every sample is used to
#' generate the posterior.
#'
#' \strong{Contol Parameters:} adapt_delta, stepsize, and max_treedepth are advanced options that give the user more control
#' over Stan's MCMC sampler. The Stan creators recommend that only advanced users change the default values, as alterations
#' can profoundly change the sampler's behavior. Refer to Hoffman & Gelman (2014, Journal of Machine Learning Research) for
#' more information on the functioning of the sampler control parameters. One can also refer to section 58.2 of the
#' \href{http://mc-stan.org/documentation/}{Stan User's Manual} for a less technical description of these arguments.
#'
#' @export
#'
#' @references
#' Samuelson, P. A. (1937). A Note on Measurement of Utility. The Review of Economic Studies, 4(2), 155. http://doi.org/10.2307/2967612
#'
#' Hoffman, M. D., & Gelman, A. (2014). The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. The
#' Journal of Machine Learning Research, 15(1), 1593-1623.
#'
#' @seealso
#' We refer users to our in-depth tutorial for an example of using hBayesDM: \url{https://rpubs.com/CCSL/hBayesDM}
#'
#' @examples
#' \dontrun{
#' # Run the model and store results in "output"
#' output <- dd_exp(data = "example", niter = 2000, nwarmup = 1000, nchain = 3, ncore = 3)
#'
#' # Visually check convergence of the sampling chains (should like like 'hairy caterpillars')
#' plot(output, type = 'trace')
#'
#' # Check Rhat values (all Rhat values should be less than or equal to 1.1)
#' rhat(output)
#'
#' # Plot the posterior distributions of the hyper-parameters (distributions should be unimodal)
#' plot(output)
#'
#' # Show the WAIC and LOOIC model fit estimates
#' printFit(output)
#' }

dd_exp <- function(data           = "choose",
niter          = 3000,
nwarmup        = 1000,
nchain         = 4,
ncore          = 1,
nthin          = 1,
inits          = "random",
indPars        = "mean",
saveDir        = NULL,
modelRegressor = FALSE,
vb             = FALSE,
inc_postpred   = FALSE,
stepsize       = 1,
max_treedepth  = 10 ) {

# Path to .stan model file
if (modelRegressor) { # model regressors (for model-based neuroimaging, etc.)
stop("** Model-based regressors are not available for this model **\n")
} else {
modelPath <- system.file("stan", "dd_exp.stan", package="hBayesDM")
}

# To see how long computations take
startTime <- Sys.time()

# For using example data
if (data=="example") {
data <- system.file("extdata", "dd_exampleData.txt", package = "hBayesDM")
} else if (data=="choose") {
data <- file.choose()
}

if (file.exists(data)) {
} else {
stop("** The data file does not exist. Please check it again. **\n  e.g., data = '/MyFolder/SubFolder/dataFile.txt', ... **\n")
}
# Remove rows containing NAs
NA_rows_all = which(is.na(rawdata), arr.ind = T)  # rows with NAs
NA_rows = unique(NA_rows_all[, "row"])
if (length(NA_rows) > 0) {
rawdata = rawdata[-NA_rows, ]
cat("The number of rows with NAs=", length(NA_rows), ". They are removed prior to modeling the data. \n", sep="")
}

# Individual Subjects
subjList <- unique(rawdata[,"subjID"])  # list of subjects x blocks
numSubjs <- length(subjList)  # number of subjects

# Specify the number of parameters and parameters of interest
numPars <- 2
POI     <- c("mu_r", "mu_beta",
"sigma",
"r", "beta",
"log_lik")

if (inc_postpred) {
POI <- c(POI, "y_pred")
}

modelName <- "dd_exp"

# Information for user
cat("\nModel name = ", modelName, "\n")
cat("Data file  = ", data, "\n")
cat("\nDetails:\n")
if (vb) {
cat(" # Using variational inference # \n")
} else {
cat(" # of chains                   = ", nchain, "\n")
cat(" # of cores used               = ", ncore, "\n")
cat(" # of MCMC samples (per chain) = ", niter, "\n")
cat(" # of burn-in samples          = ", nwarmup, "\n")
}
cat(" # of subjects                 = ", numSubjs, "\n")

################################################################################
# THE DATA.  ###################################################################
################################################################################

Tsubj <- as.vector( rep( 0, numSubjs ) ) # number of trials for each subject

for ( i in 1:numSubjs )  {
curSubj  <- subjList[ i ]
Tsubj[i] <- sum( rawdata$subjID == curSubj ) # Tsubj[N] } # Setting maxTrials maxTrials <- max(Tsubj) # Information for user continued cat(" # of (max) trials per subject = ", maxTrials, "\n\n") delay_later <- array(0, c(numSubjs, maxTrials) ) amount_later <- array(0, c(numSubjs, maxTrials) ) delay_sooner <- array(0, c(numSubjs, maxTrials) ) amount_sooner <- array(0, c(numSubjs, maxTrials) ) choice <- array(-1, c(numSubjs, maxTrials) ) for (i in 1:numSubjs) { curSubj <- subjList[i] useTrials <- Tsubj[i] tmp <- subset(rawdata, rawdata$subjID == curSubj)
delay_later[i, 1:useTrials]   <- tmp$delay_later amount_later[i, 1:useTrials] <- tmp$amount_later
delay_sooner[i, 1:useTrials]  <- tmp$delay_sooner amount_sooner[i, 1:useTrials] <- tmp$amount_sooner
choice[i, 1:useTrials] <- tmp$choice } dataList <- list( N = numSubjs, T = maxTrials, Tsubj = Tsubj, amount_later = amount_later, delay_later = delay_later, amount_sooner = amount_sooner, delay_sooner = delay_sooner, choice = choice ) # inits if (inits[1] != "random") { if (inits[1] == "fixed") { inits_fixed <- c(0.1, 1.0) } else { if (length(inits)==numPars) { inits_fixed <- inits } else { stop("Check your inital values!") } } genInitList <- function() { list( mu_p = c( qnorm(inits_fixed[1]), qnorm(inits_fixed[2]/5) ), sigma = c(1.0, 1.0), r_pr = rep(qnorm(inits_fixed[1]), numSubjs), beta_pr = rep(qnorm(inits_fixed[2]/5), numSubjs) ) } } else { genInitList <- "random" } rstan::rstan_options(auto_write = TRUE) if (ncore > 1) { numCores <- parallel::detectCores() if (numCores < ncore){ options(mc.cores = numCores) warning('Number of cores specified for parallel computing greater than number of locally available cores. Using all locally available cores.') } else{ options(mc.cores = ncore) } } else { options(mc.cores = 1) } cat("************************************\n") cat("** Building a model. Please wait. **\n") cat("************************************\n") # Fit the Stan model m = rstan::stan_model(modelPath) if (vb) { # if variational Bayesian fit = rstan::vb(m, data = dataList, pars = POI, init = genInitList) } else { fit = rstan::sampling(m, data = dataList, pars = POI, warmup = nwarmup, init = genInitList, iter = niter, chains = nchain, thin = nthin, control = list(adapt_delta = adapt_delta, max_treedepth = max_treedepth, stepsize = stepsize) ) } ## Extract parameters parVals <- rstan::extract(fit, permuted=T) if (inc_postpred) { parVals$y_pred[parVals$y_pred==-1] <- NA } r <- parVals$r
beta <- parVals$beta # Individual parameters (e.g., individual posterior means) allIndPars <- array(NA, c(numSubjs, numPars)) allIndPars <- as.data.frame(allIndPars) for (i in 1:numSubjs) { if (indPars=="mean") { allIndPars[i, ] <- c( mean(r[, i]), mean(beta[, i]) ) } else if (indPars=="median") { allIndPars[i, ] <- c( median(r[, i]), median(beta[, i]) ) } else if (indPars=="mode") { allIndPars[i, ] <- c( estimate_mode(r[, i]), estimate_mode(beta[, i]) ) } } allIndPars <- cbind(allIndPars, subjList) colnames(allIndPars) <- c("r", "beta", "subjID") # Wrap up data into a list modelData <- list(modelName, allIndPars, parVals, fit, rawdata) names(modelData) <- c("model", "allIndPars", "parVals", "fit", "rawdata") class(modelData) <- "hBayesDM" # Total time of computations endTime <- Sys.time() timeTook <- endTime - startTime # If saveDir is specified, save modelData as a file. If not, don't save # Save each file with its model name and time stamp (date & time (hr & min)) if (!is.null(saveDir)) { currTime <- Sys.time() currDate <- Sys.Date() currHr <- substr(currTime, 12, 13) currMin <- substr(currTime, 15, 16) timeStamp <- paste0(currDate, "_", currHr, "_", currMin) dataFileName = sub(pattern = "(.*)\\..*$", replacement = "\\1", basename(data))
save(modelData, file=file.path(saveDir, paste0(modelName, "_", dataFileName, "_", timeStamp, ".RData"  ) ) )
}

# Inform user of completion
cat("\n************************************\n")
cat("**** Model fitting is complete! ****\n")
cat("************************************\n")

return(modelData)
}


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hBayesDM documentation built on Sept. 12, 2018, 1:04 a.m.