Visualization of k prototypes clustering result for cluster interpretation.

1 | ```
clprofiles(object, x, vars = NULL)
``` |

`object` |
Object resulting from a call of resulting |

`x` |
Original data. |

`vars` |
Vector of either coloumn indices or variable names. |

For numerical variables boxplots and for factor variables barplots of each cluster are generated.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
# generate toy data with factors and numerics
n <- 100
prb <- 0.9
muk <- 1.5
clusid <- rep(1:4, each = n)
x1 <- sample(c("A","B"), 2*n, replace = TRUE, prob = c(prb, 1-prb))
x1 <- c(x1, sample(c("A","B"), 2*n, replace = TRUE, prob = c(1-prb, prb)))
x1 <- as.factor(x1)
x2 <- sample(c("A","B"), 2*n, replace = TRUE, prob = c(prb, 1-prb))
x2 <- c(x2, sample(c("A","B"), 2*n, replace = TRUE, prob = c(1-prb, prb)))
x2 <- as.factor(x2)
x3 <- c(rnorm(n, mean = -muk), rnorm(n, mean = muk), rnorm(n, mean = -muk), rnorm(n, mean = muk))
x4 <- c(rnorm(n, mean = -muk), rnorm(n, mean = muk), rnorm(n, mean = -muk), rnorm(n, mean = muk))
x <- data.frame(x1,x2,x3,x4)
# apply k prototyps
kpres <- kproto(x, 4)
clprofiles(kpres, x)
# in real world clusters are often not as clear cut
# by variation of lambda the emphasize is shifted towards factor / numeric variables
kpres <- kproto(x, 2)
clprofiles(kpres, x)
kpres <- kproto(x, 2, lambda = 0.1)
clprofiles(kpres, x)
kpres <- kproto(x, 2, lambda = 25)
clprofiles(kpres, x)
``` |

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