```
# NOT RUN {
require(graphics)
x <- matrix(rnorm(100), nrow = 5)
dist(x)
dist(x, diag = TRUE)
dist(x, upper = TRUE)
m <- as.matrix(dist(x))
d <- as.dist(m)
stopifnot(d == dist(x))
## Use correlations between variables "as distance"
dd <- as.dist((1 - cor(USJudgeRatings))/2)
round(1000 * dd) # (prints more nicely)
plot(hclust(dd)) # to see a dendrogram of clustered variables
## example of binary and canberra distances.
x <- c(0, 0, 1, 1, 1, 1)
y <- c(1, 0, 1, 1, 0, 1)
dist(rbind(x, y), method = "binary")
## answer 0.4 = 2/5
dist(rbind(x, y), method = "canberra")
## answer 2 * (6/5)
## To find the names
labels(eurodist)
## Examples involving "Inf" :
## 1)
x[6] <- Inf
(m2 <- rbind(x, y))
dist(m2, method = "binary") # warning, answer 0.5 = 2/4
## These all give "Inf":
stopifnot(Inf == dist(m2, method = "euclidean"),
Inf == dist(m2, method = "maximum"),
Inf == dist(m2, method = "manhattan"))
## "Inf" is same as very large number:
x1 <- x; x1[6] <- 1e100
stopifnot(dist(cbind(x, y), method = "canberra") ==
print(dist(cbind(x1, y), method = "canberra")))
## 2)
y[6] <- Inf #-> 6-th pair is excluded
dist(rbind(x, y), method = "binary" ) # warning; 0.5
dist(rbind(x, y), method = "canberra" ) # 3
dist(rbind(x, y), method = "maximum") # 1
dist(rbind(x, y), method = "manhattan") # 2.4
# }
```

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