dissassoc {TraMineR} | R Documentation |
Compute the pseudo variance (defined by a dissimilarity measure) explained by a categorical variabel.
dissassoc(diss, group, R = 1000)
diss |
A dissimilarity matrix or a dist object (see dist ) |
group |
The group variable |
R |
Number of permutation to compute the pvalue. If equal to 1, no permutation test are performed. |
The association is based on a generalization of the principe of ANOVA to any kind of distance metric. The test return a pseudo R squared that can be interpred as a usual R squared. The statistical significance of the association is computed using permutation test. This function also perform a test of pseudo-variance homogeneity (equality of variance) using a generalization of the T statistic.
There is a print method and hist method (to plot an histogramme of the significance value).
Return an object of class dissassoc
with the following componant:
groups |
A data.frame containing the number of case and the pseudo-variance of each group |
anova.table |
The pseudo ANOVA table |
stat |
The value of the statistics and their p-value |
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 pseudo variance using dissimilarities and for a basic introduction to concepts of pseudo variance analysis
disstree
to analyse dissimilarities using induction trees
dissreg
to analyse dissimilarities in a way close to linear regression
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)