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This function creates a visualization of the clustering results obtained on a sample of QTS and returns the corresponding ggplot2::ggplot object which enable further customization of the plot.

Usage

# S3 method for qtsclust
autoplot(object, ...)

Arguments

object

An object of class qtsclust as produced by kmeans.qts_sample() or hclust.qts_sample().

...

Further arguments to be passed to other methods.

Value

A ggplot2::ggplot object.

Examples

out <- kmeans(vespa64$igp[1:10], n_clusters = 2)
#>  Computing initial centroids using kmeans++ strategy...
#> Information about the data set:
#>  - Number of observations: 10
#>  - Number of dimensions: 3
#>  - Number of points: 101
#> 
#> Information about cluster initialization:
#>  - Number of clusters: 2
#>  - Initial seeds for cluster centers:          2        10
#> 
#> Information about the methods used within the algorithm:
#>  - Warping method: affine
#>  - Center method: mean
#>  - Dissimilarity method: l2
#>  - Optimization method: bobyqa
#> 
#> Information about warping parameter bounds:
#>  - Warping options:    0.1500   0.1500
#> 
#> Information about convergence criteria:
#>  - Maximum number of iterations: 100
#>  - Distance relative tolerance: 0.001
#> 
#> Information about parallelization setup:
#>  - Number of threads: 1
#>  - Parallel method: 0
#> 
#> Other information:
#>  - Use fence to robustify: 0
#>  - Check total dissimilarity: 1
#>  - Compute overall center: 0
#> 
#> Running k-centroid algorithm:
#>  - Iteration #1
#>    * Size of cluster #0: 8
#>    * Size of cluster #1: 2
#>  - Iteration #2
#>    * Size of cluster #0: 8
#>    * Size of cluster #1: 2
#> 
#> Active stopping criteria:
#>  - Memberships did not change.
ggplot2::autoplot(out)