R/summary.iio.class.R

In mokken: Conducts Mokken Scale Analysis

"summary.iio.class" <-
function(object, ...){
  if(length(object) == 2) {
    object2l <- object 
    resultsmat <- list()
    for(lvl in 1:2){
      object <- object2l[[lvl]]
      results <- object$results
      violations <- object$violations
      items.removed <- object$items.removed
      HT <- object$HT
      method <- object$method
      Hi <- object$Hi
      J <- nrow(violations)
      I.labels <- dimnames(violations)[[1]]
      summary.matrix <- matrix(0,nrow=J,ncol=10)
      summary.matrix[,1] <- round(Hi,3)
      ncat <- object$m + 1
      tmax <- rep(0,J)
      if(method=="MIIO") {
        dimnames(summary.matrix) <- list(I.labels,c("ItemH","#ac","#vi","#vi/#ac","maxvi","sum","sum/#ac",ifelse(ncat == 2,"zmax","tmax"),ifelse(ncat == 2,"#zsig","#tsig"),"crit"))
      }
      if(method=="MSCPM") {
        dimnames(summary.matrix) <- list(I.labels,c("ItemH","#ac","#vi","#vi/#ac","maxvi","sum","sum/#ac","zmax","#zsig","crit"))
      }
      if(method=="IT") {
        dimnames(summary.matrix) <- list(I.labels,c("ItemH","#ac","#vi","#vi/#ac","maxvi","sum","sum/#ac","xmax","#xsig","crit"))
      }
      k <- 0; i <- 1; j <- 2
      for(i in 1:(J-1)){for(j in (i+1):J){
        k <- k+1
        input <- results[[k]][[3]]
        rvm <- nrow(input)
        tmax[i] <- max(tmax[i],input[rvm,7])     
        tmax[j] <- max(tmax[j],input[rvm,7])     
        summary.matrix[i,c(2,3,6,9)] <- summary.matrix[i,c(2,3,6,9)] + input[rvm,c(1,2,5,8)]
        summary.matrix[j,c(2,3,6,9)] <- summary.matrix[j,c(2,3,6,9)] + input[rvm,c(1,2,5,8)]
        summary.matrix[i,c(5)] <- max(summary.matrix[i,c(5)], input[rvm,c(4)])
        summary.matrix[j,c(5)] <- max(summary.matrix[j,c(5)], input[rvm,c(4)])
        summary.matrix[i,c(8)] <- max(summary.matrix[i,c(8)], input[rvm,c(7)])
        summary.matrix[j,c(8)] <- max(summary.matrix[j,c(8)], input[rvm,c(7)])
      }}
      summary.matrix[,4] <- summary.matrix[,3]/summary.matrix[,2]
      summary.matrix[,7] <- summary.matrix[,6]/summary.matrix[,2]
      summary.matrix[,8] <- tmax
      Crit <- 50 * (.30 - summary.matrix[,1]) + 
        sqrt(summary.matrix[,3]) + 
        100 * summary.matrix[,4]  + 
        100 * summary.matrix[,5]  + 
        10 * sqrt(summary.matrix[,6]) + 
        1000 * summary.matrix[,7]  + 
        5 * summary.matrix[,8]  + 
        10 * sqrt(summary.matrix[,9])  + 
        100 * summary.matrix[,9]/summary.matrix[,2]
      Crit[summary.matrix[,3]==0] <- 0
      summary.matrix[,10] <- floor(Crit)
      summary.matrix[,c(1:6,8:10)] <- round(summary.matrix[,c(1:6,8:10)],2)
      summary.matrix[,c(7)] <- round(summary.matrix[,c(7)],4)
      resultsmat[[lvl]] <- list(method = method, item.summary = summary.matrix, backward.selection= violations, HT=HT)
    }
    resultsmatfull <- resultsmat[[1]]
    resultsmatfull[[2]] <- list("Level.1" = resultsmat[[1]][[2]], "Level.2" = resultsmat[[2]][[2]])
    resultsmat <- resultsmatfull
    #resultsmatfull[[3]] <- list("Level.1" = resultsmat[[1]][[3]], "Level.2" = resultsmat[[2]][[3]])
    #names(resultsmat) <- c("Level.1", "Level.2")
  } else {
    results <- object$results
    violations <- object$violations
    items.removed <- object$items.removed
    HT <- object$HT
    method <- object$method
    Hi <- object$Hi
    J <- nrow(violations)
    I.labels <- dimnames(violations)[[1]]
    summary.matrix <- matrix(0,nrow=J,ncol=10)
    summary.matrix[,1] <- round(Hi,3)
    ncat <- object$m + 1
    tmax <- rep(0,J)
    if(method=="MIIO") {
      dimnames(summary.matrix) <- list(I.labels,c("ItemH","#ac","#vi","#vi/#ac","maxvi","sum","sum/#ac",ifelse(ncat == 2,"zmax","tmax"),ifelse(ncat == 2,"#zsig","#tsig"),"crit"))
    }
    if(method=="MSCPM") {
      dimnames(summary.matrix) <- list(I.labels,c("ItemH","#ac","#vi","#vi/#ac","maxvi","sum","sum/#ac","zmax","#zsig","crit"))
    }
    if(method=="IT") {
      dimnames(summary.matrix) <- list(I.labels,c("ItemH","#ac","#vi","#vi/#ac","maxvi","sum","sum/#ac","xmax","#xsig","crit"))
    }
    k <- 0; i <- 1; j <- 2
    for(i in 1:(J-1)){for(j in (i+1):J){
      k <- k+1
      input <- results[[k]][[3]]
      rvm <- nrow(input)
      tmax[i] <- max(tmax[i],input[rvm,7])     
      tmax[j] <- max(tmax[j],input[rvm,7])     
      summary.matrix[i,c(2,3,6,9)] <- summary.matrix[i,c(2,3,6,9)] + input[rvm,c(1,2,5,8)]
      summary.matrix[j,c(2,3,6,9)] <- summary.matrix[j,c(2,3,6,9)] + input[rvm,c(1,2,5,8)]
      summary.matrix[i,c(5)] <- max(summary.matrix[i,c(5)], input[rvm,c(4)])
      summary.matrix[j,c(5)] <- max(summary.matrix[j,c(5)], input[rvm,c(4)])
      summary.matrix[i,c(8)] <- max(summary.matrix[i,c(8)], input[rvm,c(7)])
      summary.matrix[j,c(8)] <- max(summary.matrix[j,c(8)], input[rvm,c(7)])
    }}
    summary.matrix[,4] <- summary.matrix[,3]/summary.matrix[,2]
    summary.matrix[,7] <- summary.matrix[,6]/summary.matrix[,2]
    summary.matrix[,8] <- tmax
    Crit <- 50 * (.30 - summary.matrix[,1]) + 
      sqrt(summary.matrix[,3]) + 
      100 * summary.matrix[,4]  + 
      100 * summary.matrix[,5]  + 
      10 * sqrt(summary.matrix[,6]) + 
      1000 * summary.matrix[,7]  + 
      5 * summary.matrix[,8]  + 
      10 * sqrt(summary.matrix[,9])  + 
      100 * summary.matrix[,9]/summary.matrix[,2]
    Crit[summary.matrix[,3]==0] <- 0
    summary.matrix[,10] <- floor(Crit)
    summary.matrix[,c(1:6,8:10)] <- round(summary.matrix[,c(1:6,8:10)],2)
    summary.matrix[,c(7)] <- round(summary.matrix[,c(7)],4)
    resultsmat <- list(method = method, item.summary = summary.matrix, backward.selection= violations, HT=HT)
  }
   return(resultsmat)
}