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CircosHeatmap-aardio/dist/lib/r-library/cluster/tests/daisy-ex.Rout.save
freefire 6c0061699c add 结构调整
2025-01-12 04:36:52 +08:00

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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
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