TY - JOUR
T1 - Analyzing and visualizing state sequences in R with TraMineR
JF - Journal of Statistical Software
Y1 - 2011
A1 - Gabadinho, Alexis
A1 - Ritschard, Gilbert
A1 - Nicolas S Müller
A1 - Studer, Matthias
KW - categorical sequences
KW - dissimilarities
KW - optimal matching
KW - R
KW - representative sequences
KW - sequence complexity
KW - sequence visualization
KW - state sequences
AB - This article describes the many capabilities offered by the TraMineR toolbox for categorical sequence data. It focuses more specifically on the analysis and rendering of state sequences. Addressed features include the description of sets of sequences by means of transversal aggregated views, the computation of longitudinal characteristics of individual sequences and the measure of pairwise dissimilarities. Special emphasis is put on the multiple ways of visualizing sequences. The core element of the package is the state sequence object in which we store the set of sequences together with attributes such as the alphabet, state labels and the color palette. The functions can then easily retrieve this information to ensure presentation homogeneity across all printed and graphical displays. The article also demonstrates how TraMineR’s outcomes give access to advanced analyses such as clustering and statistical modeling of sequence data.
PB - The American Statistical Association
CY - Alexandria, VA
VL - 40
UR - http://www.jstatsoft.org/v40/i04
CP - 4
ER -
TY - JOUR
T1 - Discrepancy Analysis of State Sequences
JF - Sociological Methods & Research
Y1 - 2011
A1 - Studer, Matthias
A1 - Ritschard, Gilbert
A1 - Gabadinho, Alexis
A1 - Nicolas S Müller
KW - analysis of variance
KW - dissimilarities
KW - distance
KW - homogeneity in discrepancies
KW - Levene test
KW - optimal matching
KW - permutation test
KW - regression tree
KW - state sequence
KW - tree-structured ANOVA
AB - In this article, the authors define a methodological framework for analyzing the relationship between state sequences and covariates. Inspired by the principles of analysis of variance, this approach looks at how the covariates explain the discrepancy of the sequences. The authors use the pairwise dissimilarities between sequences to determine the discrepancy, which makes it possible to develop a series of statistical significance–based analysis tools. They introduce generalized simple and multifactor discrepancy-based methods to test for differences between groups, a pseudo-R2 for measuring the strength of sequence-covariate associations, a generalized Levene statistic for testing differences in the within-group discrepancies, as well as tools and plots for studying the evolution of the differences along the time frame and a regression tree method for discovering the most significant discriminant covariates and their interactions. In addition, the authors extend all methods to account for case weights. The scope of the proposed methodological framework is illustrated using a real-world sequence data set.
VL - 40
Y1 - 08/2011
CP - 3
J1 - Sociological Methods & Research
PY - 10.1177/0049124111415372
ER -