### Introduction

### Theory, Examples & Exercises

en:class-eval

Section: Numerical classification

Evaluates, if the sample has appropriate group membership. Samples with high *s* value are well clustered, *s* value around zero means that the sample is between two clusters, and negative *s* value means that the sample has been misclassified.

For example, we will use results of *beta flexible* numerical classification of `vltava.spe`

data (see `agnes (library cluster)`

):

## Example of silhouette function ## Following code is not necessary, if you already used examples above... # library (cluster) # dis <- vegdist (sqrt (vltava.spe), method = 'bray') # percentage cover data are transformed by square root # cluster.flexible <- agnes (x = dis, method = 'flexible', par.method = 0.625) # cluster.flexible.hclust <- as.hclust (cluster.flexible) cl <- cutree (cluster.flexible.hclust, k = 5) si <- silhouette (cl, dis) plot (si) # Group 3 has the highest number of missclassified samples, on the other hand groups 1, 2 and 5 are well defined.

# Comparison of silhouettes for single linkage, complete average linkage method. # dis <- vegdist (sqrt (vltava.spe), method = 'bray') # percentage cover data are transformed by square root # cluster.single <- hclust (d = dis, method = 'single') # cluster.complete <- hclust (dis, 'complete') # cluster.average <- hclust (dis, 'average') par (mfrow = c(1,3)) plot (silhouette (cutree (cluster.single, k = 5), dis)) plot (silhouette (cutree (cluster.complete, k = 5), dis)) plot (silhouette (cutree (cluster.average, k = 5), dis))

en/class-eval.txt · Last modified: 2019/03/22 21:59 by David Zelený