summary: Summary Method for kproto Cluster Result In clustMixType: k-Prototypes Clustering for Mixed Variable-Type Data

Description

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

Usage

 ```1 2``` ```## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```# 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) ```

clustMixType documentation built on Aug. 18, 2021, 5:08 p.m.