a vector giving a permutation of the original observations to allow for plotting, in the sense that the branches of a clustering tree will not cross.

a vector similar to order, but containing observation labels instead of observation numbers. This component is only available if the original observations were labelled.

a vector with the distances between merging clusters at the successive stages.

the agglomerative coefficient, measuring the clustering structure of the dataset.

For each observation i, denote by m(i) its dissimilarity to the first cluster it is merged with, divided by the dissimilarity of the merger in the final step of the algorithm. The ac is the average of all 1 - m(i). It can also be seen as the average width (or the percentage filled) of the banner plot. Because ac grows with the number of observations, this measure should not be used to compare datasets of very different sizes.

an (n-1) by 2 matrix, where n is the number of observations. Row i of merge describes the merging of clusters at step i of the clustering. If a number j in the row is negative, then the single observation |j| is merged at this stage. If j is positive, then the merger is with the cluster formed at stage j of the algorithm.

an object of class "dissimilarity" (see dissimilarity.object), representing the total dissimilarity matrix of the dataset.

a matrix containing the original or standardized measurements, depending on the stand option of the function agnes. If a dissimilarity matrix was given as input structure, then this component is not available.