#' RDINA Simple Q Data Analysis
#'
#' Creates a data frame that can be used to inspect the integrity of the data simulation
#'
#' @section \strong{Notation}:
#' \describe{
#' \tabular{ll}{
#' q \tab Q-matrix \cr
#' I \tab Number of examinees \cr
#' J \tab Number of items \cr
#' K \tab Number of skills \cr
#' f \tab False alarm rate \cr
#' d \tab Item descrimination (detection) \cr
#' }
#' }
#'
#' @param data Must be rDINASimpleQ data
#' @param q Must be qLow q
#'
#' @author Dave Rackham \email{ddrackham@gmail.com}
#' @references \url{http://onlinelibrary.wiley.com/doi/10.1002/j.2333-8504.2008.tb02157.x/abstract} # TODO: Update
#' @keywords q-matrix
#'
#' @export
rDINASimpleQDataAnalysis <- function(data, q){
y <- data$resp
skill1 <- q[,1]
skill2 <- q[,2]
f <- round( data$f, 3)
d <- round( data$d , 3)
probCorrectMaster <- round( boot::inv.logit(f + d), 3 )
probCorrectNonMaster <- round( boot::inv.logit(f) ,3 )
numMasters <- c(rep(600,20), rep(300,10))
numNonMasters <- data$I - numMasters
correct <- colSums(y)
incorrect <- data$I - correct
numMisses <- abs(numMasters - correct)
dataAnalysis <- data.frame(skill1, skill2, f, d, probCorrectMaster, probCorrectNonMaster, numMasters, numNonMasters, correct, incorrect, numMisses)
return(dataAnalysis)
}
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