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 perform a test of discrepancy homogeneity (equality of variance) using a generalization of the T statistic.
There is a print method and hist method (to produce 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). Analyse de dissimilarités par arbre d'induction. Revue des Nouvelles Technologies de l'Information, EGC'2009.
Batagelj, V. (1988). Generalized Ward and related clustering problems. In H. Bock (Ed.), Classification and related methods of data analysis, pp. 67-74. North-Holland, Amsterdam.
Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 32-46.
dissvar
to compute discrepancy using dissimilarities and for a basic introduction to concepts of discrepancy analysis
disstree
to analyse dissimilarities using induction trees
dissmfac
to perform multi-factor analysis of variance using dissimilarities
disscenter
to compute the distance of each object to its center of group using 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)