> #3.5 Tomato Example
> x<-rbind(
+ c(43,74,109,39),
+ c(5,112,25,82),
+ c(74,64,48,100),
+ c(64,101,91,64),
+ c(10,105,52,126),
+ c(16,12,35,26),
+ c(75,33,42,24),
+ c(35,90,100,20),
+ c(36,74,22,129),
+ c(76,37,122,19),
+ c(60,50,105,113),
+ c(57,44,119,57),
+ c(55,18,29,61),
+ c(29,24,26,21),
+ c(82,48,102,6),
+ c(91,62,48,13),
+ c(66,88,108,118),
+ c(27,50,53,91),
+ c(72,73,57,60),
+ c(108,119,82,88),
+ c(84,109,105,15),
+ c(50,50,108,32),
+ c(82,12,13,134),
+ c(39,37,74,29))
> colnames(x)<-c("Floradade","Momotaro","Summit","Rutgers")
> 
> #First do case where ties have been randomly allocated (ties in rows 4, 12 & 22)
> xnoties<-x
> xnoties[c(4,12),1]<-x[c(4,12),1]+1
> xnoties[22,2]<-x[22,2]+1
> xtotab(xnoties)
     [,1] [,2] [,3] [,4]
[1,]    4    8    9    3
[2,]    5    8    6    5
[3,]    4    5    7    8
[4,]   11    3    2    8
> U(xnoties)
     [,1] [,2] [,3] [,4]
[1,]   24    0    0    0
[2,]    0   24    0    0
[3,]    0    0   24    0
[4,]    0    0    0   24
> BlockAnalysis(xnoties)
$Cri
            Location Dispersion
Floradade -0.1581139 -1.7677670
Momotaro  -0.1581139 -0.7071068
Summit     1.1067972  0.0000000
Rutgers   -0.7905694  2.4748737

$partition
           df    SS     pvalue
Location    3  1.90 0.59341918
Dispersion  3  9.75 0.02081525
Residual    3  0.35 0.95036612
Total       9 12.00 0.21330931

> 
> #Then do case where ties are used
> xtotab(x)
     [,1] [,2] [,3] [,4]
[1,]  4.5  7.5  9.0    3
[2,]  5.0  8.5  5.5    5
[3,]  4.0  5.0  7.0    8
[4,] 10.5  3.0  2.5    8
> U(x)
     [,1] [,2] [,3] [,4]
[1,] 23.5  0.5    0    0
[2,]  0.5 22.5    1    0
[3,]  0.0  1.0   23    0
[4,]  0.0  0.0    0   24
> BlockAnalysis(x)
$Cri
            Location  Dispersion
Floradade -0.2386672 -1.60563732
Momotaro  -0.2386672 -0.71236022
Summit     1.1137802 -0.01055348
Rutgers   -0.6364458  2.32855102

$partition
           df         SS    pvalue
Location    3  1.7594937 0.6237890
Dispersion  3  8.5077895 0.0366041
Residual    3  0.8469601 0.8382049
Total       9 11.1142433 0.2679594

>