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Plotting method for Poisson kernel-based clustering

Source: R/clustering_functions.R
plot.pkbc.Rd

Plots for a pkbc object.

Usage

# S4 method for pkbc,ANY
plot(x, k = NULL, true_label = NULL, pca_res = FALSE, ...)

Arguments

x

Object of class pkbc

k

number of considered clusters. If it is not provided the scatter plot is displayed for each value of number of clusters present in the x object

true_label

factor or vector of true membership to clusters (if available). It must have the same length of final memberships.

pca_res

Logical. If TRUE the results from PCALocantore are also reported (when dimension is greater than 3).

...

Additional arguments that can be passed to the plot function

Value

The scatter-plot(s) and the elbow plot.

Details

  • scatterplot: If dimension is equal to 2 or 3, points are displayed on the circle and sphere, respectively. If dimension if greater than 3, the spherical Principal Component procedure proposed by Locantore et al. (1999), is applied for dimensionality reduction and the first three principal components are normalized and displayed on the sphere. For d > 3, the complete results from the PcaLocantore function (package rrcov) are returned if pca_res=TRUE.

  • elbow plot: the within cluster sum of squares (wcss) is computed using the Euclidean distance (left) and the cosine similarity (right).

Note

The elbow plot is commonly used as a graphical method for choosing the appropriate number of clusters. Specifically, plotting the wcss versus the number of clusters, the suggested number of clusters correspond to the point in which the plotted line has the greatest change in slope, showing an elbow.

References

Locantore, N., Marron, J.S., Simpson, D.G. et al. (1999) "Robust principal component analysis for functional data." Test 8, 1–73. https://doi.org/10.1007/BF02595862

See also

pkbc() for the clustering algorithm
pkbc for the class object definition.

Examples

dat<-matrix(rnorm(300),ncol=3)
pkbc_res<- pkbc(dat, 3)
plot(pkbc_res, 3)
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?