Tidy a G-means Clustering Object

Summarize a G-means clustering model as data frames, following the conventions of the broom package. The generics come from the generics package, so load it or broom to call the methods.

Usage

# S3 method for class 'gmeans'
tidy(x, col.names = colnames(x$centers), ...)

# S3 method for class 'gmeans'
augment(x, data, ...)

# S3 method for class 'gmeans'
glance(x, ...)

Arguments

x

(gmeans())
An object of class "gmeans".

col.names

(character())
Column names for the centers. Defaults to the column names of the centers, or x1, x2, ... if they are unnamed.

...

(any)
Additional arguments. Currently unused.

data

(matrix())
The data used to fit the model, a numeric matrix or a data frame.

Value

A data.frame():

• tidy(): one row per cluster with the centers, size, withinss, and cluster.

• augment(): data with a .cluster factor giving the cluster assignments.

• glance(): one row with k, k_init, k_max, level, totss, tot.withinss, betweenss, and iter.

See also

gmeans()

Examples

library(generics)
#> 
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
x <- as.matrix(iris[, -5])
cl <- gmeans(x)

tidy(cl)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width size  withinss cluster
#> 1     6.301031    2.886598     4.958763    1.695876   97 123.79588       1
#> 2     5.005660    3.369811     1.560377    0.290566   53  28.55208       2
glance(cl)
#>   k k_init k_max level    totss tot.withinss betweenss iter
#> 1 2      1    10 1e-04 681.3706      152.348  529.0226    1
head(augment(cl, x))
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width .cluster
#> 1          5.1         3.5          1.4         0.2        2
#> 2          4.9         3.0          1.4         0.2        2
#> 3          4.7         3.2          1.3         0.2        2
#> 4          4.6         3.1          1.5         0.2        2
#> 5          5.0         3.6          1.4         0.2        2
#> 6          5.4         3.9          1.7         0.4        2