R/fit_nodk.R

In guess: Adjust Estimates of Learning for Guessing

#' fit_nodk
#'
#' @title Goodness of fit statistics for data without don't know
#' 
#' @description For data without Don't Know, chi-square goodness of fit between true and model based multivariate distribution
#' 
#' @param pre_test data.frame carrying pre_test items
#' @param pst_test data.frame carrying pst_test items 
#' @param g 		estimates of \eqn{\gamma} produced from \code{\link{guesstimate}}
#' @param est.param estimated parameters produced from \code{\link{guesstimate}}
#' @return matrix with two rows: top row carrying chi-square value, and bottom row probability of observing that value
#' @export
#' @examples
#' \dontrun{fit_nodk(pre_test, pst_test, g, est.param)}

fit_nodk <- function(pre_test, pst_test, g, est.param) {

	data    <- multi_transmat(pre_test, pst_test)
	data    <- data[(1:nrow(data)-1),] # remove the agg.
	expec	<- matrix(ncol=nrow(data), nrow=4)
	fit		<- matrix(ncol=nrow(data), nrow=2)
	colnames(fit) <- rownames(data)
	rownames(fit) <- c("chi-square", "p-value")
		
	for (i in 1:nrow(data)) {
		
		gi			<- g[[i]]
		expec[1, i]	<- (1 - gi)*(1 - gi)*est.param[1,i]*sum(data[i,])
		expec[2, i]	<- ((1 - gi)*gi*est.param[1, i] + (1 - gi)*est.param[2, i])*sum(data[i, ])
		expec[3, i]	<- ((1 - gi)*est.param[3, i]*est.param[1, i])*sum(data[i, ])
		expec[4, i]	<- (gi*gi*est.param[1, i] + gi*est.param[2, i] + est.param[3, i])*sum(data[i, ])
		test 		<- suppressWarnings(chisq.test(expec[, i], p=data[i,]/sum(data[i, ])))
		fit[1:2, i]	<- unlist(test[c(1, 3)])
	}

	fit
}