> #3.2 Peach Example
> 
> Nij<-rbind(
+ c(10, 0, 0, 0, 0),
+ c( 0, 5, 2, 1, 2),
+ c( 0, 1, 4, 3, 2),
+ c( 0, 2, 2, 2, 4),
+ c( 0, 2, 2, 4, 2))
> rownames(Nij)<-c("A","B","C","D","E")
> colnames(Nij)<-1:5
> Nij
   1 2 3 4 5
A 10 0 0 0 0
B  0 5 2 1 2
C  0 1 4 3 2
D  0 2 2 2 4
E  0 2 2 4 2
> RBD(Nij)
$Cri
   
    Location    Dispersion Skewness      Kurtosis
  A     -4.0  3.380617e+00     -2.0  7.559289e-01
  B      0.0 -1.014185e+00      2.0 -7.559289e-01
  C      1.2 -1.352247e+00     -0.4  7.559289e-01
  D      1.6 -5.024296e-16      0.8 -4.396259e-16
  E      1.2 -1.014185e+00     -0.4 -7.559289e-01

$partition
           df        SS       pvalue
Location    4 21.440000 2.589946e-04
Dispersion  4 15.314286 4.091850e-03
Skewness    4  8.960000 6.210710e-02
Residual    4  2.285714 6.833712e-01
Total      16 48.000000 4.749992e-05

> RBD(Nij,SSeffects.max=2) #to give same level of detail as in book specify the maximum number of effects to display
$Cri
   
    Location    Dispersion Skewness      Kurtosis
  A     -4.0  3.380617e+00     -2.0  7.559289e-01
  B      0.0 -1.014185e+00      2.0 -7.559289e-01
  C      1.2 -1.352247e+00     -0.4  7.559289e-01
  D      1.6 -5.024296e-16      0.8 -4.396259e-16
  E      1.2 -1.014185e+00     -0.4 -7.559289e-01

$partition
           df       SS       pvalue
Location    4 21.44000 2.589946e-04
Dispersion  4 15.31429 4.091850e-03
Residual    8 11.24571 1.881616e-01
Total      16 48.00000 4.749992e-05

>