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 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).

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). 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.

See Also

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

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 1.2-1 Index]