Different running configurations
In this section we will try different ways of calling the
segclust() function. Minimal call only contains the data
concerned (here the dataframe simulmode) as well as
arguments lmin, ncluster and
seg.var. The function will automatically complete the
missing arguments with default values and communicate about it (not
shown here).
mode_segclust <- segclust(simulmode,
Kmax = 20, lmin=10,
ncluster = c(2,3),
seg.var = c("speed","abs_spatial_angle"))
Additionally it is advised to scale the variables used to a mean of 0
and a variance of 1. This will automatically be done for
segclust(), but can also be specified explicitly:
mode_segclust <- segclust(simulmode,
Kmax = 20, lmin=10,
ncluster = c(2,3),
seg.var = c("speed","abs_spatial_angle"),
scale.variable = TRUE)
Selecting the number of clusters and segments.
Once the segmentation/clustering have been successfully run. A
summary like this will appear:
#> ✔ Best segmentation/clustering estimated with 3 clusters and 15 segments according to BIC
#> → Number of clusters should preferentially be selected according to biological
#> knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
#> select the number of clusters.
#> → Once number of clusters is selected, the number of segments can be selected
#> according to BIC.
#> → Results of the segmentation/clustering may further be explored with plot()
#> and segmap()
As stated, the number of clusters should preferentially be selected
based on biological knowledge. Exploration of the BIC-based penalized
log-likelihood can also help selecting an appropriate number of
clusters, with the function plot_BIC(). Best-case scenario
is as below, the BIC shows a steep increase up to a maximum and a slow
decrease after the optimum and one number of clusters is clearly above
the others.
With real data, a larger number of clusters almost always improves
the penalized-likelihood so it will generally be a poor indication of
the appropriate number of clusters. Such situation may be shown with our
dataset simulmode indeed, if we allow the number of
clusters to be tested between 2 and 5 instead of a maximum at three, we
obtain the following BIC curve:
mode_segclust <- segclust(simulmode,
Kmax = 20, lmin=10,
ncluster = 2:5,
seg.var = c("speed","abs_spatial_angle"),
scale.variable = TRUE)
plot_BIC(mode_segclust)
In the curve above we see that the optimum number of clusters
selected is 4, despite the difference being relatively small compared to
ncluster = 3 and to the evolution with the number of
segments. In addition, if we look at the results of the segmentation we
can see identical segment distribution, the only difference is that
state 2 in the segmentation with 3 clusters is divided into state 2 and
3 in the segmentation with 4 clusters:
plot(mode_segclust, ncluster = 3)
plot(mode_segclust, ncluster = 4)
More generally, as in this example, if the selected number of
segments for a higher number of clusters is the same, then the lower
number of clusters should be favored.
Once the number of clusters have been selected, the number of
segments can be selected with the BIC criterium. When the
segmentation-clustering is reliable, the selected optimum should be a
maximum just before a linear drop of the penalized log-Likelihood as in
the examples above. With real data the signal is seldom as clear and may
require adjustment based on exploration of the realized
segmentation/clustering.
