summary.kproto {clustMixType} R Documentation

## Summary Method for kproto Cluster Result

### Description

Investigation of variances to specify lambda for k-prototypes clustering.

### Usage

```## S3 method for class 'kproto'
summary(object, data = NULL, pct.dig = 3, ...)
```

### Arguments

 `object` Object of class `kproto`. `data` Optional data set to be analyzed. If `!(is.null(data))` clusters for `data` are assigned by `predict(object, data)`. If not specified the clusters of the original data ara analyzed which is only possible if `kproto` has been called using `keep.data = TRUE`. `pct.dig` Number of digits for rounding percentages of factor variables. `...` Further arguments to be passed to internal call of `summary()` for numeric variables.

### Details

For numeric variables statistics are computed for each clusters using `summary()`. For categorical variables distribution percentages are computed.

### Value

List where each element corresponds to one variable. Each row of any element corresponds to one cluster.

### Examples

```# 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)

res <- kproto(x, 4)
summary(res)

```

[Package clustMixType version 0.2-14 Index]