dissassoc {TraMineR}R Documentation

Analysis of discrepancy based on dissimilarity measure

Description

Compute the discrepancy (defined by a dissimilarity measure) 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.

Details

The association is based on a generalization of the ANOVA principle to any kind of distance metric. The test returns a pseudo R-squared that can be interpreted as a usual R-squared. The statistical significance of the association is computed by means of permutation tests. This function also performs a test of discrepancy homogeneity (equality of variance) using a generalization of the T statistic.
There are print and hist methods (the latter producing an histogram of the significance values).

Value

Returns an object of class dissassoc with the following components:

groups A data frame containing 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

References

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.

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)

[Package TraMineR version 1.4-1 Index]