(Version: 2.0-8) Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
TraMineR provides tools for both state sequences and event
sequences. The first step when using the package is to define a
state sequence object (with
seqdef) if you want to
explore state sequences, and an event sequence object (with
seqecreate) if you are interested in event
State sequences are defined from a series of variables giving the states at the successive positions, while event sequences are defined from (vertical) time stamped event data. The package, however, can handle many other different data organizations and provides tools to help converting state sequences into event sequences and vice versa.
Alexis Gabadinho, Matthias Studer, Nicolas S. M<c3><bc>ller, Reto B<c3><bc>rgin, Pierre-Alexandre Fonta, and Gilbert Ritschard
Gabadinho, A., G. Ritschard, N. S. M<c3><bc>ller and M. Studer (2011). Analyzing and Visualizing State Sequences in R with TraMineR. Journal of Statistical Software 40(4), 1-37.
Gabadinho, A., G. Ritschard, M. Studer and N. S. M<c3><bc>ller (2009).
Mining Sequence Data in
R with the
A user's guide. Department of Econometrics and Laboratory of
Demography, University of Geneva
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## load the mvad data library(TraMineR) data(mvad) ## create a state sequence object from columns 17 to 86 mvad.seq <- seqdef(mvad[,17:86]) ## distribution plot by sex (male) seqdplot(mvad.seq, group=mvad$male, border=NA) ## compute the LCS pairwise distance matrix ## among the first 10 sequences mvad.lcs <- seqdist(mvad.seq[1:10,], method="LCS")
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