> #2.6 Bradley et al Example
> #Book should use mid-rank scores but uses natural scores instead
> 
> Nij<-rbind(
+ c( 9, 5, 9,13, 4),
+ c( 7, 3,10,20, 4),
+ c(14,13, 6, 7, 0),
+ c(11,15, 3, 5, 8),
+ c( 0, 2,10,30, 2))
> rownames(Nij)<-c("A","B","C","D","E")
> colnames(Nij)<-c("Terrible","Poor","Fair","Good","Excellent")
> Nij
  Terrible Poor Fair Good Excellent
A        9    5    9   13         4
B        7    3   10   20         4
C       14   13    6    7         0
D       11   15    3    5         8
E        0    2   10   30         2
> CRD(Nij) #default analysis is to use mid-rank scores
$Cri
   
       Location   Dispersion    Skewness   Kurtosis
  A -0.05652348  0.333589116 -0.09268201  1.1760449
  B  1.48205805 -0.321406258 -1.11228994  1.2369000
  C -3.93914797 -0.006408533  0.38827575 -1.0497646
  D -1.60230340  2.576582401  3.30409748 -1.6605199
  E  3.89312910 -2.507890890 -2.39767870  0.2650383

$partition
           df        SS       pvalue
Location    4 35.440408 3.771584e-07
Dispersion  4 13.142918 1.059818e-02
Skewness    4 18.062460 1.199888e-03
Residual    4  6.842581 1.444447e-01
Total      16 73.488367 2.424975e-09

> CRD(Nij,rankscores=F) #to match book force code not (F=false) to use mid-rank scores so to use natural scores instead
$Cri
   
       Location Dispersion   Skewness   Kurtosis
  A -0.03497861  0.3778368 -0.2481326  1.1404360
  B  1.50412078 -0.1311193 -1.3394617  0.9965839
  C -3.95258262 -0.2992177  0.6420426 -0.8031391
  D -1.69654058  2.2477940  3.6943790 -1.1407344
  E  3.95540410 -2.1399547 -2.6455562 -0.2036768

$partition
           df        SS       pvalue
Location    4 36.409984 2.382795e-07
Dispersion  4  9.881468 4.247238e-02
Skewness    4 22.915350 1.316534e-04
Residual    4  4.281566 3.692392e-01
Total      16 73.488367 2.424975e-09

>