TraMineR (version 1.6-2)

dissassoc: Analysis of discrepancy based on dissimilarity measure

Description

Compute and test the share of discrepancy (defined from a dissimilarity matrix) explained by a categorical variable.

Usage

dissassoc(diss, group, R = 1000)

Arguments

diss

A dissimilarity matrix or a dist object (see dist)

group

The grouping variable

R

Number of permutations for computing the p-value. If equal to 1, no permutation test is performed.

Value

• Returns an object of class dissassoc with the following components:
• groups
  A data frame with the number of cases and the discrepancy of each group
• anova.table
  The pseudo ANOVA table
• stat
  The value of the statistics and their p-values
• perms
  The permutation object, see boot

encoding

latin1

Details

The dissassoc function assesses the association between objects characterized by their dissimilarity matrix and a discrete covariate. It provides a generalization of the ANOVA principle to any kind of distance metric. The function returns a pseudo R-square that can be interpreted as a usual R-square. The statistical significance of the association is computed by means of permutation tests. The function performs also a test of discrepancy homogeneity (equality of within variances) using a generalization of the Bartlett's statistics. There are print and hist methods (the latter producing an histogram of the permuted values used for testing the significance).

References

Studer, M., G. Ritschard, A. Gabadinho and N. S. M�ller (2009) Discrepancy analysis of complex objects using dissimilarities. In F. Guillet, G. Ritschard, H. Briand, and D. A. Zighed (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence, Volume 292, pp. 3-19. Berlin: Springer. Studer, M., G. Ritschard, A. Gabadinho and N. S. M�ller (2009). Analyse de dissimilarit�s 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.

See Also

dissvar to compute the pseudo variance from dissimilarities and for a basic introduction to concepts of pseudo variance analysis. 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.

Examples

## Defining a state sequence object
data(mvad)
mvad.seq <- seqdef(mvad[, 17:86])

## Building dissimilarities
mvad.lcs <- seqdist(mvad.seq, method="LCS")

## R=1 imply no permutation test
da <- dissassoc(mvad.lcs, group=mvad$gcse5eq, R=10)
print(da)
hist(da)