dissvar {TraMineR} | R Documentation |
Compute the discrepancy from the pairwise dissimilarities between objects. The discrepancy is a measure of dispersion of the set of objects.
dissvar(diss)
diss |
A dissimilarity matrix or a dist object (see dist ) |
The discrepancy is an extension of the concept of variance to other kind of objects for which we have a dissimilarity measure. The discrepancy s^2 is defined as:
s^2=(1/(2n^2)) * sum sum d_ij
Mathematical ground: In the Euclidean case, the sum of squares can be expressed as:
SS= sum (y_i - y_mean)^2=(1/(2n)) * sum sum (y_i - y_j)^2
The concept of discrepancy generalizes the equation by allowing to replace the term (y_i - y_j)^2 with any measure of dissimilarity d_{ij}.
The pseudo variance.
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.
Anderson, M. J. (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 32-46.
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.
disscenter
to compute the distance of each object to its group center from pairwise dissimilarities.
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") ## Pseudo variance of the sequences print(dissvar(mvad.lcs))