seqdiff: Position-wise discrepancy analysis between groups of...

View source: R/seqdiff.R

seqdiffR Documentation

Position-wise discrepancy analysis between groups of sequences


The function analyses how the differences between groups of sequences evolve along the positions. It runs a sequence of discrepancy analyses on sliding windows.


seqdiff(seqdata, group, cmprange = c(0, 1),
  seqdist.args = list(method = "LCS", norm = "auto"), with.missing = FALSE,
  weighted = TRUE, squared = FALSE, seqdist_arg)



a state sequence object created with the seqdef function.


The group variable.


Vector of two integers: Time range of the sliding windows. Comparison at t is computed on the window (t + cmprange[1], t + cmprange[2]).


List of arguments passed to seqdist for computing the distances.


Logical. If TRUE, missing values are considered as an additional state. If FALSE subsequences with missing values are removed from the analysis.


Logical. If TRUE, seqdiff uses the weights specified in seqdata.


Logical. If TRUE the dissimilarities are squared for computing the discrepancy.


Deprecated. Use seqdist.args instead.


The function analyses how the part of discrepancy explained by the group variable evolves along the position axis. It runs successively discrepancy analyses within a sliding time-window of range cmprange). At each position t, the method uses seqdist to compute a distance matrix over the time-window (t + cmprange[1], t + cmprange[2]) and then derives the explained discrepancy on that window with dissassoc.

There are print and plot methods for the returned value.


A seqdiff object, with the following items:


A data.frame with five statistics (Pseudo F, Pseudo Fbf, Pseudo R2, Bartlett, and Levene) for each time stamp of the sequence (see dissassoc)


A data.frame with, at each time position t, the discrepancy within the whole set of sequences and within each group (defined by the group variable).


Matthias Studer (with Gilbert Ritschard for the help page)


Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2011). Discrepancy analysis of state sequences, Sociological Methods and Research, Vol. 40(3), 471-510, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0049124111415372")}.

Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2010) Discrepancy analysis of complex objects using dissimilarities. In F. Guillet, G. Ritschard, D. A. Zighed and H. Briand (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence, Volume 292, pp. 3-19. 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.

See Also

dissassoc to analyse the association of the group variable with the whole sequence


## Define a state sequence object
## First 12 months of first 100 trajectories
mvad.seq <- seqdef(mvad[1:100, 17:28])

## Position-wise discrepancy analysis using
##  centered sliding windows of length 5.
mvad.diff <- seqdiff(mvad.seq, group=mvad$gcse5eq[1:100], cmprange=c(-2,2))
plot(mvad.diff, stat=c("Pseudo R2", "Levene"))
plot(mvad.diff, stat="discrepancy")

TraMineR documentation built on May 29, 2024, 5 a.m.