plot.pkbc {QuadratiK} | R Documentation |
Plotting method for Poisson kernel-based clustering
Description
Plots for a pkbc object.
Usage
## S4 method for signature 'pkbc,ANY'
plot(x, k = NULL, true_label = NULL, pca_res = FALSE, ...)
Arguments
x |
Object of class |
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 |
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 |
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 (packagerrcov
) are returned ifpca_res=TRUE
.elbow plot: the within cluster sum of squares (wcss) is computed using the Euclidean distance (left) and the cosine similarity (right).
Value
The scatter-plot(s) and the elbow plot.
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)