R version 4.2.0 Patched (2022-05-13 r82353) -- "Vigorous Calisthenics"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> ## For different cluster versions
>
> require(cluster)
Loading required package: cluster
> 
> if(interactive()) print(packageDescription("cluster"))
> 
> ## trivial cases should 'work':
> daisy(cbind(1))
Dissimilarities :
dissimilarity(0)

Metric :  euclidean 
Number of objects : 1
> (d10 <- daisy(matrix(0., 1,0))); str(d10)
Dissimilarities :
dissimilarity(0)

Metric :  euclidean 
Number of objects : 1
 'dissimilarity' num(0) 
 - attr(*, "Size")= int 1
 - attr(*, "Metric")= chr "euclidean"
> d01 <- daisy(matrix(0., 0,1))
> if(paste(R.version$major, R.version$minor, sep=".") >= "2.1.0")
+     print(d01)
Dissimilarities :
dissimilarity(0)

Metric :  euclidean 
Number of objects : 0
> str(d01)
 'dissimilarity' num(0) 
 - attr(*, "Size")= int 0
 - attr(*, "Metric")= chr "euclidean"
> d32 <- data.frame(eins=c("A"=1,"B"=1,"C"=1), zwei=c(2,2,2))
> daisy(d32)
Dissimilarities :
  A B
B 0  
C 0 0

Metric :  euclidean 
Number of objects : 3
> daisy(d32, stand = TRUE)
Dissimilarities :
  A B
B 0  
C 0 0

Metric :  euclidean 
Number of objects : 3
Warning message:
In daisy(d32, stand = TRUE) :
  'x' has constant columns 1, 2; these are standardized to 0
> daisy(d32, type = list(ordratio="zwei"))
Dissimilarities :
  A B
B 0  
C 0 0

Metric :  mixed ;  Types = I, T 
Number of objects : 3
> 
> 
> str(d5 <- data.frame(a= c(0, 0, 0,1,0,0, 0,0,1, 0,NA),
+                      b= c(NA,0, 1,1,0,1, 0,1,0, 1,0),
+                      c= c(0, 1, 1,0,1,NA,1,0,1, 0,NA),
+                      d= c(1, 1, 0,1,0,0, 0,0,0, 1,0),
+                      e= c(1, NA,0,1,0,0, 0,0,NA,1,1)))
'data.frame':	11 obs. of  5 variables:
 $ a: num  0 0 0 1 0 0 0 0 1 0 ...
 $ b: num  NA 0 1 1 0 1 0 1 0 1 ...
 $ c: num  0 1 1 0 1 NA 1 0 1 0 ...
 $ d: num  1 1 0 1 0 0 0 0 0 1 ...
 $ e: num  1 NA 0 1 0 0 0 0 NA 1 ...
> (d0 <- daisy(d5))
Dissimilarities :
          1        2        3        4        5        6        7        8
2  1.290994                                                               
3  1.936492 1.581139                                                      
4  1.118034 1.936492 2.000000                                             
5  1.936492 1.118034 1.000000 2.236068                                    
6  1.825742 1.825742 0.000000 1.936492 1.118034                           
7  1.936492 1.118034 1.000000 2.236068 0.000000 1.118034                  
8  1.581139 1.936492 1.000000 1.732051 1.414214 0.000000 1.414214         
9  2.236068 1.581139 1.581139 1.936492 1.118034 1.825742 1.118034 1.936492
10 0.000000 1.581139 1.732051 1.000000 2.000000 1.581139 2.000000 1.414214
11 1.581139 1.581139 1.825742 1.825742 1.290994 1.825742 1.290994 1.825742
          9       10
2                   
3                   
4                   
5                   
6                   
7                   
8                   
9                   
10 2.236068         
11 0.000000 1.825742

Metric :  euclidean 
Number of objects : 11
Warning message:
In daisy(d5) : binary variable(s) 1, 2, 3, 4, 5 treated as interval scaled
> (d1 <- daisy(d5, type = list(asymm = 1:5)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.5000000                                                            
3  1.0000000 0.6666667                                                  
4  0.3333333 0.7500000 0.8000000                                        
5  1.0000000 0.5000000 0.5000000 1.0000000                              
6  1.0000000 1.0000000 0.0000000 0.7500000 1.0000000                    
7  1.0000000 0.5000000 0.5000000 1.0000000 0.0000000 1.0000000          
8  1.0000000 1.0000000 0.5000000 0.7500000 1.0000000 0.0000000 1.0000000
9  1.0000000 0.6666667 0.6666667 0.7500000 0.5000000 1.0000000 0.5000000
10 0.0000000 0.6666667 0.7500000 0.2500000 1.0000000 0.6666667 1.0000000
11 0.5000000 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000
           8         9        10
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9  1.0000000                    
10 0.6666667 1.0000000          
11 1.0000000        NA 0.6666667

Metric :  mixed ;  Types = A, A, A, A, A 
Number of objects : 11
> (d2 <- daisy(d5, type = list(symm = 1:2, asymm= 3:5)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.3333333                                                            
3  0.7500000 0.5000000                                                  
4  0.3333333 0.7500000 0.8000000                                        
5  0.7500000 0.2500000 0.3333333 1.0000000                              
6  0.6666667 0.6666667 0.0000000 0.7500000 0.5000000                    
7  0.7500000 0.2500000 0.3333333 1.0000000 0.0000000 0.5000000          
8  0.6666667 0.7500000 0.3333333 0.7500000 0.6666667 0.0000000 0.6666667
9  1.0000000 0.5000000 0.6666667 0.7500000 0.3333333 1.0000000 0.3333333
10 0.0000000 0.5000000 0.6000000 0.2500000 0.8000000 0.5000000 0.8000000
11 0.5000000 0.5000000 1.0000000 0.6666667 0.5000000 1.0000000 0.5000000
           8         9        10
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9  1.0000000                    
10 0.5000000 1.0000000          
11 1.0000000 0.0000000 0.6666667

Metric :  mixed ;  Types = S, S, A, A, A 
Number of objects : 11
> (d2.<- daisy(d5, type = list(     asymm= 3:5)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.3333333                                                            
3  0.7500000 0.5000000                                                  
4  0.3333333 0.7500000 0.8000000                                        
5  0.7500000 0.2500000 0.3333333 1.0000000                              
6  0.6666667 0.6666667 0.0000000 0.7500000 0.5000000                    
7  0.7500000 0.2500000 0.3333333 1.0000000 0.0000000 0.5000000          
8  0.6666667 0.7500000 0.3333333 0.7500000 0.6666667 0.0000000 0.6666667
9  1.0000000 0.5000000 0.6666667 0.7500000 0.3333333 1.0000000 0.3333333
10 0.0000000 0.5000000 0.6000000 0.2500000 0.8000000 0.5000000 0.8000000
11 0.5000000 0.5000000 1.0000000 0.6666667 0.5000000 1.0000000 0.5000000
           8         9        10
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9  1.0000000                    
10 0.5000000 1.0000000          
11 1.0000000 0.0000000 0.6666667

Metric :  mixed ;  Types = I, I, A, A, A 
Number of objects : 11
Warning message:
In daisy(d5, type = list(asymm = 3:5)) :
  binary variable(s) 1, 2 treated as interval scaled
> stopifnot(identical(c(d2), c(d2.)))
> (dS <- daisy(d5, stand = TRUE))# gave error in some versions
Dissimilarities :
          1        2        3        4        5        6        7        8
2  2.614264                                                               
3  4.010913 3.291786                                                      
4  3.493856 4.725761 4.757684                                             
5  4.010913 2.415752 2.000000 5.160965                                    
6  3.823025 3.801028 0.000000 4.813384 2.236068                           
7  4.010913 2.415752 2.000000 5.160965 0.000000 2.236068                  
8  3.310837 3.995202 2.025000 4.305222 2.846160 0.000000 2.846160         
9  5.558018 4.247692 4.148136 3.995202 3.493856 4.789855 3.493856 4.725761
10 0.000000 3.182103 3.587469 3.125000 4.107303 3.310837 4.107303 2.961302
11 3.416389 3.416389 3.674376 3.801028 2.614264 3.674376 2.614264 3.674376
          9       10
2                   
3                   
4                   
5                   
6                   
7                   
8                   
9                   
10 5.307417         
11 0.000000 3.801028

Metric :  euclidean 
Number of objects : 11
Warning message:
In daisy(d5, stand = TRUE) :
  binary variable(s) 1, 2, 3, 4, 5 treated as interval scaled
> stopifnot(all.equal(as.vector(summary(c(dS), digits=9)),
+                     c(0, 2.6142638, 3.4938562, 3.2933687, 4.0591077, 5.5580177),
+                     tol = 1e-7))# 7.88e-9
> 
> d5[,4] <- 1 # binary with only one instead of two values
> (d0 <- daisy(d5))
Dissimilarities :
          1        2        3        4        5        6        7        8
2  1.290994                                                               
3  1.581139 1.118034                                                      
4  1.118034 1.936492 1.732051                                             
5  1.581139 0.000000 1.000000 2.000000                                    
6  1.290994 1.290994 0.000000 1.581139 1.118034                           
7  1.581139 0.000000 1.000000 2.000000 0.000000 1.118034                  
8  1.118034 1.581139 1.000000 1.414214 1.414214 0.000000 1.414214         
9  1.825742 1.118034 1.581139 1.581139 1.118034 1.825742 1.118034 1.936492
10 0.000000 1.581139 1.414214 1.000000 1.732051 1.118034 1.732051 1.000000
11 0.000000 0.000000 1.825742 1.290994 1.290994 1.825742 1.290994 1.825742
          9       10
2                   
3                   
4                   
5                   
6                   
7                   
8                   
9                   
10 1.936492         
11 0.000000 1.290994

Metric :  euclidean 
Number of objects : 11
Warning message:
In daisy(d5) : binary variable(s) 1, 2, 3, 5 treated as interval scaled
> (d1 <- daisy(d5, type = list(asymm = 1:5)))# 2 NAs
Dissimilarities :
           1         2         3         4         5         6         7
2  1.0000000                                                            
3  1.0000000 0.5000000                                                  
4  0.5000000 1.0000000 0.7500000                                        
5  1.0000000 0.0000000 0.5000000 1.0000000                              
6  1.0000000 1.0000000 0.0000000 0.6666667 1.0000000                    
7  1.0000000 0.0000000 0.5000000 1.0000000 0.0000000 1.0000000          
8  1.0000000 1.0000000 0.5000000 0.6666667 1.0000000 0.0000000 1.0000000
9  1.0000000 0.5000000 0.6666667 0.6666667 0.5000000 1.0000000 0.5000000
10 0.0000000 1.0000000 0.6666667 0.3333333 1.0000000 0.5000000 1.0000000
11 0.0000000        NA 1.0000000 0.5000000 1.0000000 1.0000000 1.0000000
           8         9        10
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9  1.0000000                    
10 0.5000000 1.0000000          
11 1.0000000        NA 0.5000000

Metric :  mixed ;  Types = A, A, A, A, A 
Number of objects : 11
Warning message:
In daisy(d5, type = list(asymm = 1:5)) :
  at least one binary variable has not 2 different levels.
> (d2 <- daisy(d5, type = list(symm = 1:2, asymm= 3:5)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.5000000                                                            
3  0.6666667 0.3333333                                                  
4  0.5000000 1.0000000 0.7500000                                        
5  0.6666667 0.0000000 0.3333333 1.0000000                              
6  0.5000000 0.5000000 0.0000000 0.6666667 0.5000000                    
7  0.6666667 0.0000000 0.3333333 1.0000000 0.0000000 0.5000000          
8  0.5000000 0.6666667 0.3333333 0.6666667 0.6666667 0.0000000 0.6666667
9  1.0000000 0.3333333 0.6666667 0.6666667 0.3333333 1.0000000 0.3333333
10 0.0000000 0.6666667 0.5000000 0.3333333 0.7500000 0.3333333 0.7500000
11 0.0000000 0.0000000 1.0000000 0.5000000 0.5000000 1.0000000 0.5000000
           8         9        10
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9  1.0000000                    
10 0.3333333 1.0000000          
11 1.0000000 0.0000000 0.5000000

Metric :  mixed ;  Types = S, S, A, A, A 
Number of objects : 11
Warning message:
In daisy(d5, type = list(symm = 1:2, asymm = 3:5)) :
  at least one binary variable has not 2 different levels.
> (d2.<- daisy(d5, type = list(     asymm= 3:5)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.5000000                                                            
3  0.6666667 0.3333333                                                  
4  0.5000000 1.0000000 0.7500000                                        
5  0.6666667 0.0000000 0.3333333 1.0000000                              
6  0.5000000 0.5000000 0.0000000 0.6666667 0.5000000                    
7  0.6666667 0.0000000 0.3333333 1.0000000 0.0000000 0.5000000          
8  0.5000000 0.6666667 0.3333333 0.6666667 0.6666667 0.0000000 0.6666667
9  1.0000000 0.3333333 0.6666667 0.6666667 0.3333333 1.0000000 0.3333333
10 0.0000000 0.6666667 0.5000000 0.3333333 0.7500000 0.3333333 0.7500000
11 0.0000000 0.0000000 1.0000000 0.5000000 0.5000000 1.0000000 0.5000000
           8         9        10
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9  1.0000000                    
10 0.3333333 1.0000000          
11 1.0000000 0.0000000 0.5000000

Metric :  mixed ;  Types = I, I, A, A, A 
Number of objects : 11
Warning messages:
1: In daisy(d5, type = list(asymm = 3:5)) :
  at least one binary variable has not 2 different levels.
2: In daisy(d5, type = list(asymm = 3:5)) :
  binary variable(s) 1, 2 treated as interval scaled
> ## better leave away the constant variable: it has no effect:
> stopifnot(identical(c(d1), c(daisy(d5[,-4], type = list(asymm = 1:4)))))
> 
> ###---- Trivial "binary only" matrices (not data frames) did fail:
> 
> x <- matrix(0, 2, 2)
> dimnames(x)[[2]] <- c("A", "B")## colnames<- is missing in S+
> daisy(x, type = list(symm= "B", asymm="A"))
Dissimilarities :
  1
2 0

Metric :  mixed ;  Types = A, S 
Number of objects : 2
Warning message:
In daisy(x, type = list(symm = "B", asymm = "A")) :
  at least one binary variable has not 2 different levels.
> daisy(x, type = list(symm= "B"))# 0 too
Dissimilarities :
  1
2 0

Metric :  mixed ;  Types = I, S 
Number of objects : 2
Warning message:
In daisy(x, type = list(symm = "B")) :
  at least one binary variable has not 2 different levels.
> 
> x2 <- x; x2[2,2] <- 1
> daisy(x2, type= list(symm = "B"))# |-> 0.5  (gives 1 in S+)
Dissimilarities :
    1
2 0.5

Metric :  mixed ;  Types = I, S 
Number of objects : 2
> daisy(x2, type= list(symm = "B", asymm="A"))# 1
Dissimilarities :
  1
2 1

Metric :  mixed ;  Types = A, S 
Number of objects : 2
Warning message:
In daisy(x2, type = list(symm = "B", asymm = "A")) :
  at least one binary variable has not 2 different levels.
> 
> x3 <- x; x3[] <- diag(2)
> daisy(x3) # warning: both as interval scaled -> sqrt(2)
Dissimilarities :
         1
2 1.414214

Metric :  euclidean 
Number of objects : 2
> daisy(x3, type= list(symm="B", asymm="A"))#  1
Dissimilarities :
  1
2 1

Metric :  mixed ;  Types = A, S 
Number of objects : 2
> daisy(x3, type= list(symm =c("B","A")))   #  1, S+: sqrt(2)
Dissimilarities :
  1
2 1

Metric :  mixed ;  Types = S, S 
Number of objects : 2
> daisy(x3, type= list(asymm=c("B","A")))   #  1, S+ : sqrt(2)
Dissimilarities :
  1
2 1

Metric :  mixed ;  Types = A, A 
Number of objects : 2
> 
> x4 <- rbind(x3, 1)
> daisy(x4, type= list(symm="B", asymm="A"))# 1   0.5 0.5
Dissimilarities :
    1   2
2 1.0    
3 0.5 0.5

Metric :  mixed ;  Types = A, S 
Number of objects : 3
> daisy(x4, type= list(symm=c("B","A")))    # dito;  S+ : 1.41  1   1
Dissimilarities :
    1   2
2 1.0    
3 0.5 0.5

Metric :  mixed ;  Types = S, S 
Number of objects : 3
> daisy(x4, type= list(asymm=c("A","B")))   # dito,     dito
Dissimilarities :
    1   2
2 1.0    
3 0.5 0.5

Metric :  mixed ;  Types = A, A 
Number of objects : 3
> 
> 
> 
> ## ----------- example(daisy) -----------------------
> 
> data(flower)
> data(agriculture)
> 
> ## Example 1 in ref:
> ##  Dissimilarities using Euclidean metric and without standardization
> (d.agr  <- daisy(agriculture, metric = "euclidean", stand = FALSE))
Dissimilarities :
            B        DK         D        GR         E         F       IRL
DK   5.408327                                                            
D    2.061553  3.405877                                                  
GR  22.339651 22.570113 22.661200                                        
E    9.818350 11.182576 10.394710 12.567418                              
F    3.448188  3.512834  2.657066 20.100995  8.060397                    
IRL 12.747549 13.306014 13.080138  9.604166  3.140064 10.564563          
I    5.803447  5.470832  5.423099 17.383325  5.727128  2.773085  7.920859
L    4.275512  2.220360  2.300000 24.035391 12.121056  4.060788 14.569145
NL   1.649242  5.096077  2.435159 20.752349  8.280097  2.202272 11.150785
P   17.236299 17.864490 17.664088  5.162364  7.430343 15.164432  4.601087
UK   2.828427  8.052950  4.850773 21.485344  8.984431  5.303772 12.103718
            I         L        NL         P
DK                                         
D                                          
GR                                         
E                                          
F                                          
IRL                                        
I                                          
L    6.660330                              
NL   4.204759  4.669047                    
P   12.515990 19.168985 15.670673          
UK   6.723095  7.102112  3.124100 16.323296

Metric :  euclidean 
Number of objects : 12
> (d.agr2 <- daisy(agriculture, metric = "manhattan"))
Dissimilarities :
       B   DK    D   GR    E    F  IRL    I    L   NL    P
DK   7.5                                                  
D    2.7  4.8                                             
GR  30.4 31.9 31.5                                        
E   13.6 15.1 14.7 16.8                                   
F    4.3  3.8  3.4 28.1 11.3                              
IRL 17.2 18.7 18.3 13.2  3.6 14.9                         
I    6.0  7.5  7.1 24.4  7.6  3.7 11.2                    
L    5.0  2.5  2.3 33.8 17.0  5.7 20.6  9.4               
NL   2.0  6.3  3.1 28.4 11.6  3.1 15.2  4.4  5.4          
P   23.7 25.2 24.8  6.7 10.1 21.4  6.5 17.7 27.1 21.7     
UK   3.2 10.7  5.9 28.0 11.2  7.5 14.8  8.8  8.2  4.4 21.3

Metric :  manhattan 
Number of objects : 12
> 
> 
> ## Example 2 in ref
> (dfl0 <- daisy(flower))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.8875408                                                            
3  0.5272467 0.5147059                                                  
4  0.3517974 0.5504493 0.5651552                                        
5  0.4115605 0.6226307 0.3726307 0.6383578                              
6  0.2269199 0.6606209 0.3003268 0.4189951 0.3443627                    
7  0.2876225 0.5999183 0.4896242 0.3435866 0.4197712 0.1892974          
8  0.4234069 0.4641340 0.6038399 0.2960376 0.4673203 0.5714869 0.4107843
9  0.5808824 0.4316585 0.4463644 0.8076797 0.3306781 0.5136846 0.5890931
10 0.6094363 0.4531046 0.4678105 0.5570670 0.3812908 0.4119281 0.5865196
11 0.3278595 0.7096814 0.5993873 0.6518791 0.3864788 0.4828840 0.5652369
12 0.4267565 0.5857843 0.6004902 0.5132761 0.5000817 0.5248366 0.6391340
13 0.5196487 0.5248366 0.5395425 0.7464461 0.2919118 0.4524510 0.5278595
14 0.2926062 0.5949346 0.6096405 0.3680147 0.5203431 0.3656863 0.5049837
15 0.6221814 0.3903595 0.5300654 0.5531454 0.4602124 0.5091503 0.3345588
16 0.6935866 0.3575163 0.6222222 0.3417892 0.7301471 0.5107843 0.4353758
17 0.7765114 0.1904412 0.5801471 0.4247141 0.6880719 0.5937092 0.5183007
18 0.4610294 0.4515114 0.7162173 0.4378268 0.4755310 0.6438317 0.4692402
           8         9        10        11        12        13        14
2                                                                       
3                                                                       
4                                                                       
5                                                                       
6                                                                       
7                                                                       
8                                                                       
9  0.6366422                                                            
10 0.6639706 0.4256127                                                  
11 0.4955474 0.4308007 0.3948121                                        
12 0.4216503 0.4194036 0.3812092 0.2636029                              
13 0.5754085 0.2181781 0.3643791 0.3445670 0.2331699                    
14 0.4558007 0.4396650 0.3609477 0.2838644 0.1591503 0.3784314          
15 0.4512255 0.2545343 0.4210784 0.4806781 0.4295752 0.3183007 0.4351307
16 0.6378268 0.6494690 0.3488562 0.7436683 0.6050654 0.5882353 0.4598039
17 0.4707516 0.6073938 0.3067810 0.7015931 0.5629902 0.5461601 0.5427288
18 0.1417892 0.5198529 0.8057598 0.5359477 0.5495507 0.5733252 0.5698121
          15        16        17
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9                               
10                              
11                              
12                              
13                              
14                              
15                              
16 0.3949346                    
17 0.3528595 0.1670752          
18 0.5096814 0.7796160 0.6125408

Metric :  mixed ;  Types = N, N, N, N, O, O, I, I 
Number of objects : 18
> stopifnot(identical(c(dfl0),
+                     c(daisy(flower, type = list(symm = 1)))) &&
+           identical(c(dfl0),
+                     c(daisy(flower, type = list(symm = 2)))) &&
+           identical(c(dfl0),
+                     c(daisy(flower, type = list(symm = 3)))) &&
+           identical(c(dfl0),
+                     c(daisy(flower, type = list(symm = c(1,3)))))
+          )
> 
> (dfl1 <- daisy(flower, type = list(asymm = 3)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.8875408                                                            
3  0.5272467 0.5882353                                                  
4  0.3517974 0.5504493 0.5651552                                        
5  0.4115605 0.7115780 0.4258637 0.6383578                              
6  0.2269199 0.7549953 0.3432306 0.4189951 0.3935574                    
7  0.2876225 0.6856209 0.5595705 0.3435866 0.4797386 0.2163399          
8  0.4234069 0.4641340 0.6038399 0.2960376 0.4673203 0.5714869 0.4107843
9  0.5808824 0.4933240 0.5101307 0.8076797 0.3779178 0.5870682 0.6732493
10 0.6094363 0.5178338 0.5346405 0.5570670 0.4357610 0.4707750 0.6703081
11 0.3278595 0.7096814 0.5993873 0.6518791 0.3864788 0.4828840 0.5652369
12 0.4267565 0.5857843 0.6004902 0.5132761 0.5000817 0.5248366 0.6391340
13 0.5196487 0.5998133 0.6166200 0.7464461 0.3336134 0.5170868 0.6032680
14 0.2926062 0.5949346 0.6096405 0.3680147 0.5203431 0.3656863 0.5049837
15 0.6221814 0.4461251 0.6057890 0.5531454 0.5259570 0.5818861 0.3823529
16 0.6935866 0.4085901 0.7111111 0.3417892 0.8344538 0.5837535 0.4975724
17 0.7765114 0.2176471 0.6630252 0.4247141 0.7863679 0.6785247 0.5923436
18 0.4610294 0.4515114 0.7162173 0.4378268 0.4755310 0.6438317 0.4692402
           8         9        10        11        12        13        14
2                                                                       
3                                                                       
4                                                                       
5                                                                       
6                                                                       
7                                                                       
8                                                                       
9  0.6366422                                                            
10 0.6639706 0.4864146                                                  
11 0.4955474 0.4308007 0.3948121                                        
12 0.4216503 0.4194036 0.3812092 0.2636029                              
13 0.5754085 0.2493464 0.4164332 0.3445670 0.2331699                    
14 0.4558007 0.4396650 0.3609477 0.2838644 0.1591503 0.3784314          
15 0.4512255 0.2908964 0.4812325 0.4806781 0.4295752 0.3637722 0.4351307
16 0.6378268 0.7422502 0.3986928 0.7436683 0.6050654 0.6722689 0.4598039
17 0.4707516 0.6941643 0.3506069 0.7015931 0.5629902 0.6241830 0.5427288
18 0.1417892 0.5198529 0.8057598 0.5359477 0.5495507 0.5733252 0.5698121
          15        16        17
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9                               
10                              
11                              
12                              
13                              
14                              
15                              
16 0.4513539                    
17 0.4032680 0.1909430          
18 0.5096814 0.7796160 0.6125408

Metric :  mixed ;  Types = N, N, A, N, O, O, I, I 
Number of objects : 18
> (dfl2 <- daisy(flower, type = list(asymm = c(1, 3), ordratio = 7)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.9007353                                                            
3  0.6176471 0.5882353                                                  
4  0.4226891 0.5455882 0.6403361                                        
5  0.4806723 0.7369748 0.5264706 0.7605042                              
6  0.2823529 0.7470588 0.3911765 0.4764706 0.4980392                    
7  0.3310924 0.6983193 0.6676471 0.4109244 0.5745098 0.2764706          
8  0.5100840 0.4544118 0.6789916 0.3327731 0.5705882 0.6563025 0.4932773
9  0.5808824 0.5084034 0.5252101 0.8257353 0.3882353 0.6100840 0.6756303
10 0.6323529 0.5067227 0.5235294 0.5522059 0.4722689 0.4739496 0.6941176
11 0.3389706 0.7117647 0.6014706 0.6588235 0.4066176 0.4919118 0.5742647
12 0.4441176 0.5816176 0.5963235 0.5139706 0.5264706 0.5220588 0.6544118
13 0.5286765 0.6252101 0.6420168 0.7735294 0.3336134 0.5504202 0.6159664
14 0.3044118 0.5963235 0.6110294 0.3742647 0.5411765 0.3573529 0.5147059
15 0.6242647 0.4588235 0.6184874 0.5691176 0.5386555 0.6025210 0.3823529
16 0.6845588 0.3831933 0.6857143 0.3147059 0.8344538 0.5504202 0.4848739
17 0.7897059 0.2176471 0.6630252 0.4198529 0.8117647 0.6705882 0.6050420
18 0.5268908 0.4647059 0.8336134 0.5210084 0.5537815 0.7588235 0.5386555
           8         9        10        11        12        13        14
2                                                                       
3                                                                       
4                                                                       
5                                                                       
6                                                                       
7                                                                       
8                                                                       
9  0.6595588                                                            
10 0.6639706 0.5126050                                                  
11 0.5073529 0.4419118 0.4066176                                        
12 0.4272059 0.4367647 0.3867647 0.2698529                              
13 0.6073529 0.2596639 0.4529412 0.3647059 0.2595588                    
14 0.4669118 0.4514706 0.3720588 0.2845588 0.1647059 0.3992647          
15 0.4720588 0.2932773 0.5050420 0.4897059 0.4448529 0.3764706 0.4448529
16 0.6058824 0.7319328 0.3621849 0.7235294 0.5786765 0.6722689 0.4389706
17 0.4610294 0.7092437 0.3394958 0.7036765 0.5588235 0.6495798 0.5441176
18 0.1882353 0.5198529 0.8286765 0.5470588 0.5669118 0.5823529 0.5816176
          15        16        17
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9                               
10                              
11                              
12                              
13                              
14                              
15                              
16 0.4386555                    
17 0.4159664 0.1655462          
18 0.5117647 0.7705882 0.6257353

Metric :  mixed ;  Types = A, N, A, N, O, O, T, I 
Number of objects : 18
> (dfl3 <- daisy(flower, type = list(asymm = 1:3)))
Dissimilarities :
           1         2         3         4         5         6         7
2  0.8875408                                                            
3  0.6025677 0.5882353                                                  
4  0.4020542 0.6290850 0.6458917                                        
5  0.4703548 0.7115780 0.4968410 0.7295518                              
6  0.2593371 0.7549953 0.4004357 0.4788515 0.4591503                    
7  0.3287115 0.7998911 0.6528322 0.4581155 0.5596950 0.2523965          
8  0.4838936 0.5304388 0.6901027 0.3947168 0.5340803 0.6531279 0.5477124
9  0.5808824 0.4933240 0.5101307 0.8076797 0.3779178 0.5870682 0.6732493
10 0.6094363 0.5178338 0.5346405 0.5570670 0.4357610 0.4707750 0.6703081
11 0.3278595 0.7096814 0.5993873 0.6518791 0.3864788 0.4828840 0.5652369
12 0.4267565 0.5857843 0.6004902 0.5132761 0.5000817 0.5248366 0.6391340
13 0.5196487 0.5998133 0.6166200 0.7464461 0.3336134 0.5170868 0.6032680
14 0.2926062 0.5949346 0.6096405 0.3680147 0.5203431 0.3656863 0.5049837
15 0.6221814 0.5204793 0.6057890 0.6321662 0.5259570 0.5818861 0.4460784
16 0.6935866 0.4766885 0.7111111 0.3906162 0.8344538 0.5837535 0.5805011
17 0.7765114 0.2539216 0.6630252 0.4853875 0.7863679 0.6785247 0.6910675
18 0.5268908 0.5160131 0.8185341 0.5837691 0.5434641 0.7358077 0.6256536
           8         9        10        11        12        13        14
2                                                                       
3                                                                       
4                                                                       
5                                                                       
6                                                                       
7                                                                       
8                                                                       
9  0.6366422                                                            
10 0.6639706 0.4864146                                                  
11 0.4955474 0.4308007 0.3948121                                        
12 0.4216503 0.4194036 0.3812092 0.2636029                              
13 0.5754085 0.2493464 0.4164332 0.3445670 0.2331699                    
14 0.4558007 0.4396650 0.3609477 0.2838644 0.1591503 0.3784314          
15 0.5156863 0.2908964 0.4812325 0.4806781 0.4295752 0.3637722 0.4351307
16 0.7289449 0.7422502 0.3986928 0.7436683 0.6050654 0.6722689 0.4598039
17 0.5380019 0.6941643 0.3506069 0.7015931 0.5629902 0.6241830 0.5427288
18 0.1890523 0.5198529 0.8057598 0.5359477 0.5495507 0.5733252 0.5698121
          15        16        17
2                               
3                               
4                               
5                               
6                               
7                               
8                               
9                               
10                              
11                              
12                              
13                              
14                              
15                              
16 0.5265795                    
17 0.4704793 0.2227669          
18 0.5824930 0.8909897 0.7000467

Metric :  mixed ;  Types = A, A, A, N, O, O, I, I 
Number of objects : 18
> 
> ## --- animals
> data(animals)
> d0 <- daisy(animals)
Warning message:
In daisy(animals) :
  binary variable(s) 1, 2, 3, 4, 5, 6 treated as interval scaled
> 
> d1 <- daisy(animals - 1, type=list(asymm=c(2,4)))
Warning message:
In daisy(animals - 1, type = list(asymm = c(2, 4))) :
  binary variable(s) 1, 3, 5, 6 treated as interval scaled
> (d2 <- daisy(animals - 1, type=list(symm = c(1,3,5,6), asymm=c(2,4))))
Dissimilarities :
          ant       bee       cat       cpl       chi       cow       duc
bee 0.4000000                                                            
cat 1.0000000 0.8000000                                                  
cpl 0.5000000 0.4000000 0.5000000                                        
chi 0.8000000 0.6666667 0.4000000 0.8000000                              
cow 0.7500000 0.6000000 0.2500000 0.7500000 0.2000000                    
duc 0.6000000 0.6000000 0.6000000 1.0000000 0.5000000 0.4000000          
eag 0.8333333 0.8333333 0.5000000 0.8333333 0.5000000 0.6666667 0.3333333
ele 0.6000000 0.8333333 0.6000000 1.0000000 0.2000000 0.4000000 0.3333333
fly 0.4000000 0.4000000 0.8000000 0.4000000 1.0000000 1.0000000 0.6000000
fro 0.5000000 0.8000000 0.7500000 0.7500000 0.5000000 0.7500000 0.6000000
her 0.2500000 0.6000000 0.7500000 0.7500000 0.6000000 0.5000000 0.4000000
lio 0.7500000 0.6000000 0.2500000 0.7500000 0.0000000 0.0000000 0.4000000
liz 0.5000000 0.8000000 0.5000000 0.5000000 0.8000000 0.7500000 0.6000000
lob 0.0000000 0.5000000 1.0000000 0.3333333 1.0000000 1.0000000 0.7500000
man 0.8000000 0.6666667 0.4000000 0.8000000 0.0000000 0.2000000 0.5000000
rab 0.7500000 0.6000000 0.2500000 0.7500000 0.2000000 0.0000000 0.4000000
sal 0.3333333 0.7500000 0.6666667 0.6666667 0.7500000 0.6666667 0.5000000
spi 0.5000000 0.4000000 0.5000000 0.0000000 0.7500000 0.7500000 1.0000000
wha 0.6000000 0.8333333 0.6000000 1.0000000 0.2000000 0.4000000 0.3333333
          eag       ele       fly       fro       her       lio       liz
bee                                                                      
cat                                                                      
cpl                                                                      
chi                                                                      
cow                                                                      
duc                                                                      
eag                                                                      
ele 0.3333333                                                            
fly 0.5000000 0.8333333                                                  
fro 0.4000000 0.2500000 0.6000000                                        
her 0.6666667 0.4000000 0.6000000 0.2500000                              
lio 0.6000000 0.2500000 1.0000000 0.6666667 0.5000000                    
liz 0.5000000 0.6000000 0.4000000 0.2500000 0.2500000 0.7500000          
lob 0.8000000 0.7500000 0.2500000 0.5000000 0.3333333 1.0000000 0.3333333
man 0.5000000 0.2000000 1.0000000 0.5000000 0.6000000 0.0000000 0.8000000
rab 0.6666667 0.4000000 1.0000000 0.7500000 0.5000000 0.0000000 0.7500000
sal 0.6000000 0.5000000 0.5000000 0.2500000 0.0000000 0.6666667 0.0000000
spi 0.8000000 1.0000000 0.4000000 0.6666667 0.7500000 0.7500000 0.5000000
wha 0.3333333 0.0000000 0.8333333 0.2500000 0.4000000 0.2500000 0.6000000
          lob       man       rab       sal       spi
bee                                                  
cat                                                  
cpl                                                  
chi                                                  
cow                                                  
duc                                                  
eag                                                  
ele                                                  
fly                                                  
fro                                                  
her                                                  
lio                                                  
liz                                                  
lob                                                  
man 1.0000000                                        
rab 1.0000000 0.2000000                              
sal 0.3333333 0.7500000 0.6666667                    
spi 0.3333333 0.7500000 0.7500000 0.6666667          
wha 0.7500000 0.2000000 0.4000000 0.5000000 1.0000000

Metric :  mixed ;  Types = S, A, S, A, S, S 
Number of objects : 20
> stopifnot(c(d1) == c(d2))
> 
> d3 <- daisy(2 - animals, type=list(asymm=c(2,4)))
Warning message:
In daisy(2 - animals, type = list(asymm = c(2, 4))) :
  binary variable(s) 1, 3, 5, 6 treated as interval scaled
> (d4 <- daisy(2 - animals, type=list(symm = c(1,3,5,6), asymm=c(2,4))))
Dissimilarities :
          ant       bee       cat       cpl       chi       cow       duc
bee 0.3333333                                                            
cat 0.6666667 0.6666667                                                  
cpl 0.3333333 0.3333333 0.3333333                                        
chi 0.6666667 0.6666667 0.3333333 0.6666667                              
cow 0.5000000 0.5000000 0.1666667 0.5000000 0.1666667                    
duc 0.5000000 0.6000000 0.5000000 0.8333333 0.5000000 0.3333333          
eag 0.8333333 1.0000000 0.5000000 0.8333333 0.6000000 0.6666667 0.4000000
ele 0.5000000 0.8333333 0.5000000 0.8333333 0.2000000 0.3333333 0.3333333
fly 0.3333333 0.4000000 0.6666667 0.3333333 1.0000000 0.8333333 0.6000000
fro 0.4000000 0.8000000 0.6000000 0.6000000 0.5000000 0.6000000 0.6000000
her 0.1666667 0.5000000 0.5000000 0.5000000 0.5000000 0.3333333 0.3333333
lio 0.6000000 0.6000000 0.2000000 0.6000000 0.0000000 0.0000000 0.4000000
liz 0.3333333 0.6666667 0.3333333 0.3333333 0.6666667 0.5000000 0.5000000
lob 0.0000000 0.4000000 0.6000000 0.2000000 0.8000000 0.6000000 0.6000000
man 0.6666667 0.6666667 0.3333333 0.6666667 0.0000000 0.1666667 0.5000000
rab 0.5000000 0.5000000 0.1666667 0.5000000 0.1666667 0.0000000 0.3333333
sal 0.2000000 0.6000000 0.4000000 0.4000000 0.6000000 0.4000000 0.4000000
spi 0.4000000 0.4000000 0.4000000 0.0000000 0.6000000 0.6000000 1.0000000
wha 0.5000000 0.8333333 0.5000000 0.8333333 0.2000000 0.3333333 0.3333333
          eag       ele       fly       fro       her       lio       liz
bee                                                                      
cat                                                                      
cpl                                                                      
chi                                                                      
cow                                                                      
duc                                                                      
eag                                                                      
ele 0.4000000                                                            
fly 0.6000000 0.8333333                                                  
fro 0.5000000 0.2500000 0.6000000                                        
her 0.6666667 0.3333333 0.5000000 0.2000000                              
lio 0.6000000 0.2000000 1.0000000 0.5000000 0.4000000                    
liz 0.5000000 0.5000000 0.3333333 0.2000000 0.1666667 0.6000000          
lob 0.8000000 0.6000000 0.2000000 0.4000000 0.2000000 0.7500000 0.2000000
man 0.6000000 0.2000000 1.0000000 0.5000000 0.5000000 0.0000000 0.6666667
rab 0.6666667 0.3333333 0.8333333 0.6000000 0.3333333 0.0000000 0.5000000
sal 0.6000000 0.4000000 0.4000000 0.2000000 0.0000000 0.5000000 0.0000000
spi 0.8000000 0.8000000 0.4000000 0.5000000 0.6000000 0.6000000 0.4000000
wha 0.4000000 0.0000000 0.8333333 0.2500000 0.3333333 0.2000000 0.5000000
          lob       man       rab       sal       spi
bee                                                  
cat                                                  
cpl                                                  
chi                                                  
cow                                                  
duc                                                  
eag                                                  
ele                                                  
fly                                                  
fro                                                  
her                                                  
lio                                                  
liz                                                  
lob                                                  
man 0.8000000                                        
rab 0.6000000 0.1666667                              
sal 0.2000000 0.6000000 0.4000000                    
spi 0.2500000 0.6000000 0.6000000 0.5000000          
wha 0.6000000 0.2000000 0.3333333 0.4000000 0.8000000

Metric :  mixed ;  Types = S, A, S, A, S, S 
Number of objects : 20
> stopifnot(c(d3) == c(d4))
> 
> pairs(cbind(d0,d2,d4),
+       main = "Animals -- symmetric and asymm. dissimilarities")
> 
> proc.time()
   user  system elapsed 
  0.518   0.113   0.905