Assign k-Prototypes Clusters
Predicts k-prototypes cluster memberships and distances for new data.
## S3 method for class 'kproto' predict(object, newdata, ...)
object |
Object resulting from a call of |
newdata |
New data frame (of same structure) where cluster memberships are to be predicted. |
... |
Currently not used. |
kmeans like object of class kproto:
cluster |
Vector of cluster memberships. |
dists |
Matrix with distances of observations to all cluster prototypes. |
# 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)
predicted.clusters <- predict(kpres, x)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.