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Assessing the quality of clusters in a partition estimate is added by this
function. The result can then be plotted with
plot.salso.summary. The current implementation of the
calculation of these summaries is not terribly efficient and may be improved
in the future.
# S3 method for salso.estimate
summary(object, alternative, orderingMethod = 1, ...)A list containing the estimate, the pairwise similarity matrix, the mean pairwise similarity matrix, the score and mean pairwise similarity for each observation, exemplar observation for each cluster, a dendrogram object, a vector for ordering observations in the heatmap plot, the size of each cluster, and the number of clusters.
An object returned by the salso function.
An optional argument specifying an alternative clustering
to use instead of that provided by object. Use this feature to
obtain numerical and graphical summaries of a clustering estimate from
other procedures. This clustering must be provided in canonical form:
cluster labels as integers starting at 1 for the first observation and
incrementing by one for each new label.
An integer giving method to use to order the
observations for a heatmap plot. Currently values 1 or 2 are
supported.
Currently ignored.
# For examples, use 'nCores=1' per CRAN rules, but in practice omit this.
data(iris.clusterings)
draws <- iris.clusterings
est <- salso(draws, nCores=1)
summ <- summary(est)
plot(summ, type="heatmap")
plot(summ, type="mds")
plot(summ, type="pairs", data=iris)
plot(summ, type="dendrogram")
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