seqdiff {TraMineR} | R Documentation |
Decompose the difference between groups of sequences
seqdiff(seqdata, group, cmprange = c(0, 1), seqdist_arg=list(method="LCS",norm=TRUE))
seqdata |
The sequence to analyse |
group |
The group variable |
cmprange |
The range used to compare subsequences |
seqdist_arg |
argument passed directly to seqdist as a list |
Analyse for each timestamp the discrepancy of a subsequence (defined by cmprange
) explained by the group
variable.
The method compute a distance matrix, using seqdist
for each timestamp and then apply dissassoc to compute the discrepancy explained.
There are print and plot method for the result returned.
A seqdiff
object, with the following item:
stat |
A data.frame with tree statistics, PseudoF, PseudoR2 and PseudoT for each timestamp of the sequence, see dissassoc |
variance |
A data.frame with the discrepancy of each group defined by the group variable and for the whole population at each timestamp |
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.
dissassoc
to analyse the association with the whole sequence
## Defining a state sequence object data(mvad) mvad.seq <- seqdef(mvad[, 17:86]) ## Building dissimilarities mvad.diff <- seqdiff(mvad.seq, group=mvad$gcse5eq) print(mvad.diff) plot(mvad.diff) plot(mvad.diff, stat="Variance")