dissassoc(diss, group, weights=NULL, R=1000,
weight.permutation="replicate", squared=FALSE)dist)dissmfac."diss"
(attach weights to the dissimilarity matrix), "replicate" (replicate case using weights), "rounded-replicate" (replicate case using rounded weights), <TRUE the dissimilarities diss are squared.dissassoc with the
following components:dissassoc function assesses the association
between objects characterized by their dissimilarity matrix and a
discrete covariate. It provides a generalization of the ANOVA
principle to any kind of distance metric. The function returns a
pseudo R-square that can be interpreted as a usual R-square. The
statistical significance of the association is computed by means of
permutation tests. The function performs also a test of discrepancy
homogeneity (equality of within variances) using a generalization of
the Levene statistic and Bartlett's statistics.
There are
print and hist methods (the latter producing an
histogram of the permuted values used for testing the significance).
If a numeric group variable is provided, it will be treated as categorical, i.e., each different value will be considered as a different category. To measure the `linear' effect of a numerical variable, use dissmfac.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 (any dissimilarity measure can be used)
mvad.ham <- seqdist(mvad.seq, method="HAM")
## R=1 implies no permutation test
da <- dissassoc(mvad.ham, group=mvad$gcse5eq, R=10)
print(da)
hist(da)Run the code above in your browser using DataLab