| run_kmeans {opm} | R Documentation |
Run a k-means partitioning analysis. This function is
used by discrete in ‘gap’ mode to
automatically determine the range of ambiguous data. If
applied to such one-dimensional data, it uses an exact
algorithm from the Ckmeans.1d.dp package.
## S4 method for signature 'matrix,numeric' run_kmeans(object, k, cores = 1L, nstart = 10L, ...) ## S4 method for signature 'numeric,numeric' run_kmeans(object, k, cores = 1L)
object |
Numeric vector or matrix. |
k |
Numeric vector. Number of clusters requested. |
nstart |
Numeric scalar. Ignored if
‘Ckmeans.1d.dp’ is called. Otherwise passed to
|
cores |
Numeric scalar indicating the number of cores to use. |
... |
List of optional arguments passed to
|
S3 object of class kmeanss, basically a named list
of kmeans objects.
Wang, H., Song, M. 2011 Ckmeans.1d.dp: Optimal k-means clustering in one dimension by dynamic programming. The R Journal 3, p. 29–33.
stats::kmeans Ckmeans.1d.dp::Ckmeans.1d.dp
Other kmeans-functions: borders,
calinski,
hist.Ckmeans.1d.dp,
hist.kmeans, hist.kmeanss,
plot.kmeanss, to_kmeans,
x <- as.vector(extract(vaas_4, as.labels = NULL, subset = "A"))
summary(x.km <- run_kmeans(x, k = 1:10)) # => 'kmeanss' object
## Length Class Mode
## 1 9 kmeans list
## 2 9 kmeans list
## 3 9 kmeans list
## 4 9 kmeans list
## 5 9 kmeans list
## 6 9 kmeans list
## 7 9 kmeans list
## 8 9 kmeans list
## 9 9 kmeans list
## 10 9 kmeans list
stopifnot(inherits(x.km, "kmeanss"), length(x.km) == 10)
stopifnot(sapply(x.km, class) == "kmeans", names(x.km) == 1:10)