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##' Fit model parameters
##'
##' Estimates parameters for proximal/distal model using multi-criteria estimation (\code{\link[=nsga2]{mco}})
##'
##' @param dataX Object of class \code{\link{TimedVector}} specifying trials including
##' whether signal was rewarded/unrewarded and times
##' @param dataResponse Corresponding observations of subject's response to signal
##' @param responseFunction The function to use to transform the mean estimate into a response
##' @param sessionBoundaries (optional) Vector defining how to group the trials into sessions
##' where the items are the starting indicies for each session (so the last value can be the index
##' after the last trial) and \code{NA}s are used for gaps between sessions
##' @param fitG \code{TRUE} (default) to estimate g, or \code{FALSE} to fix g at 0
##' @return Model fit
##' @seealso \code{\link{computeModel}}
##' @export
##' @importFrom mco nsga2
##' @author Chloe Bracis
fitModel = function( dataX, dataResponse,
responseFunction = calculateResponse,
sessionBoundaries = NA, fitG = TRUE )
{
nDatasets = length( dataX )
nParameters = 6
if ( fitG ) { nParameters = nParameters + 1 }
upperBounds = c( 1, 1, 1, 1, 10, 100 )
if ( fitG ) { upperBounds = c( upperBounds, 10000 ) }
lowerBounds = rep( 0, nParameters )
fit = nsga2( modelObjectiveFunction, idim = nParameters, odim = nDatasets,
nDatasets, dataX, dataResponse, responseFunction, sessionBoundaries, fitG,
lower.bounds = lowerBounds,
upper.bounds = upperBounds,
popsize = 200, generations = 200 )
return( fit )
}
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