Summary Method for kproto Cluster Result
Investigation of variances to specify lambda for k-prototypes clustering.
## S3 method for class 'kproto' summary(object, data = NULL, pct.dig = 3, ...)
object |
Object of class |
data |
Optional data set to be analyzed. If |
pct.dig |
Number of digits for rounding percentages of factor variables. |
... |
Further arguments to be passed to internal call of |
For numeric variables statistics are computed for each clusters using summary().
For categorical variables distribution percentages are computed.
List where each element corresponds to one variable. Each row of any element corresponds to one cluster.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.