disscenter {TraMineR} | R Documentation |
Compute the dissimilarity between a set of objects and their group center using a pairwise dissimilarity matrix.
disscenter(diss, group=NULL, medoids.index=NULL, allcenter = FALSE)
diss |
a dissimilarity matrix such as generated by seqdist , or a dist object (see dist |
group |
if null, only one group is considered, otherwise group to compute center |
medoids.index |
if NULL, return dissimilarity to center. If equal to "first", return the index of the first encountered most central sequence. One index per group is returned. If equal to "all", all medoids index are returned. If group is set, one list per group is returned. |
allcenter |
logical. If TRUE , returns a data.frame containing the dissimilarity between each object and its group center, each column corresponding to a group. |
This function computes the dissimilarity between given objects and their group center. The group center may not belong to the space formed by the objects (in the same way, the average do not belong to a space formed by discrete measure).
This distance can also be understood as the contribution to the discrepancy (see dissvar
).
The dissimilarity between a given object and its group center may be negative if the dissimilarity measure does not respect the triangle inequality.
It can be shown that this dissimilarity is equal to Batagelj (1988):
d_(xg)=1/n *(sum d_xi - SS)
Where SS is the sum of squares (see dissvar
).
A vector with the dissimilarity to center of group for each sequence, or a list of medoid indexes.
Studer, M., G. Ritschard, A. Gabadinho, and N. S. Müller (2009) Discrepancy analysis of complex objects using dissimilarities. In H. Briand, F. Guillet, G. Ritschard, and D. A. Zighed (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence. Berlin: Springer.
Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2009) Analyse de dissimilarités par arbre d'induction. In EGC 2009, Revue des Nouvelles Technologies de l'Information, Vol. E-15, pp. 7–18.
Batagelj, V. (1988) Generalized ward and related clustering problems. In H. Bock (Ed.), Classification and related methods of data analysis, Amsterdam: North-Holland, pp. 67–74.
dissvar
to compute the pseudo variance from dissimilarities and for a basic introduction to concepts of pseudo variance analysis
dissassoc
to test association between objects represented by their dissimilarities and a covariate.
disstree
for an induction tree analyse of objects characterized by a dissimilarity matrix.
dissmfac
to perform multi-factor analysis of variance from pairwise dissimilarities.
## Defining a state sequence object data(mvad) mvad.seq <- seqdef(mvad[, 17:86]) ## Building dissimilarities mvad.lcs <- seqdist(mvad.seq, method="LCS") ## Compute distance to center according to group gcse5eq dc <- disscenter(mvad.lcs, group=mvad$gcse5eq) ## Ploting distribution of dissimilarity to center boxplot(dc~mvad$gcse5eq, col="cyan") ## Retrieving index of the first medoids, one per group dc <- disscenter(mvad.lcs, group=mvad$Grammar, medoids.index="first") print(dc) ## Retrieving index of all medoids in each group dc <- disscenter(mvad.lcs, group=mvad$Grammar, medoids.index="all") print(dc)