dissassoc {TraMineR} | R Documentation |
Compute the discrepancy (defined by a dissimilarity measure) explained by a categorical variable.
dissassoc(diss, group, R = 1000)
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. |
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).
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 |
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.
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.
## 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)