R/bigIRT-package.R

In cdriveraus/bigIRT: Fits item response theory models to big data

#' The 'bigIRT' package.
#'
#' @description A DESCRIPTION OF THE PACKAGE
#'
#' @docType package
#' @name bigIRT-package
#' @aliases bigIRT
#' @useDynLib bigIRT, .registration = TRUE
#' @import methods
#' @import Rcpp data.table mize parallel
#' @importFrom rstan sampling
#'
#' @references
#' Stan Development Team (NA). RStan: the R interface to Stan. R package version 2.26.1. https://mc-stan.org
#'@examples
#' #Generate some data (here 2pl model
#' require(data.table)
#' dat <- simIRT(Nsubs = 5000,Nitems = 100,Nscales = 1,
#'   logitCMean = -10,logitCSD = 0,AMean = 1,ASD = .3,
#'   BMean=0,BSD = .5,
#'   AbilityMean = 0,AbilitySD = 1)
#'
#' #convert to wide for TAM
#' wdat <- data.frame(dcast(data.table(dat$dat),formula = 'id ~ Item',value.var='score')[,-1])
#'
#'
#' #fit using TAM
#' require(TAM)
#' tfit <-tam.mml.2pl(resp = wdat,est.variance = TRUE)
#'
#'
#' #fit using bigIRT
#' fit <- fitIRT(dat$dat,cores=2,pl=2)
#'
#' #some summary stuff:
#' plot(dat$Ability,(fit$pars$Ability-dat$Ability)^2) #ability error given ability
#' sqrt(mean((fit$pars$Ability-dat$Ability)^2)) #rms error stat
#'
#' #correlations of estimated vs true
#' cor(data.frame(True=dat$Ability,Est=fit$pars$Ability))
#' cor(data.frame(True=dat$A,Est=fit$pars$A))
#' cor(data.frame(True=dat$B,Est=fit$pars$B))
#'
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