ErrorMeasuresfForAnalysis2.R

# compute error measures for predictive model for activity (act) as dependent variable
# note this is computed for all 6 activities and a total computed which is an aggregate value

errormeasures <- function(orig, pred, act) {
    N <- length(orig)
    origtrue  <-as.vector(NULL)
    origfalse <-as.vector(NULL)
    predtrue  <-as.vector(NULL)
    predfalse <-as.vector(NULL)
    
    for (i in 1:N) {
              
        if(orig[i] == act){
          origtrue <- append(origtrue, i)          
        } else {
          origfalse <- append(origfalse, i)          
        }

        if(pred[i] == act){
          predtrue <- append(predtrue, i)          
        } else {
          predfalse <- append(predfalse, i)          
        }

    }

    # compute the members of the quadrant
    truepos <- length(intersect(origtrue, predtrue))
    trueneg <- length(intersect(origfalse, predfalse))
    falsepos <- length(intersect(origfalse, predtrue))
    falseneg <- length(intersect(origtrue, predfalse))

    # compute the 4 measures as defined in class
    #
    # PosPred:    Positive Predictive Value
    # NegPred:    Negative Predictive Value
    # Sens:       Sensitivity
    # Spec:       Specificity

    pospred <- truepos/(truepos + falsepos)
    negpred <- trueneg/(trueneg + falseneg)
    sens    <- truepos/(truepos + falseneg)
    spec    <- trueneg/(trueneg + falsepos)
    
    c(truepos,trueneg,falsepos,falseneg,pospred,negpred,sens,spec)

}