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 se- quence 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.

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Analyzing and Visualizing State Sequences in R with TraMineR

https://orbit.dtu.dk/files/146992779/1_s2.0_S0092867418303751_main.pdf

Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal

Comparing text strings in terms of distance functions is a common and fundamental task in many statistical text-processing applications. Thus far, string distance functionality has been somewhat scattered around R and its extension packages, leaving users with inconistent interfaces and encoding handling. The stringdist package was designed to offer a low-level interface to several popular string distance algorithms which have been re-implemented in C for this purpose. The package offers distances based on counting q-grams, edit-based distances, and some lesser known heuristic distance functions. Based on this functionality, the package also offers inexact matching equivalents of R’s native exact matching functions match and %in%.

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The stringdist Package for Approximate String Matching

This is a comparative study of the multiple ways of measuring dissimilarities between state sequences. The originality of the study is the focus put on the differences between sequences that are sociologically important when studying life courses such as family life trajectories or professional careers. These differences essentially concern the sequencing (the order in which successive states appear), the timing and the duration of the spells in successive states. The study examines the sensitivity of the measures to these three aspects analytically and empirically by means of simulations. Even if some distance measures underperform, the study shows that there is no universally optimal distance index, and that the choice of a measure depends on which aspect we want to focus on. From the review and simulation results, the paper derives guidelines to help the end user to choose the right dissimilarity measure for her or his research objectives. This study also introduces novel ways of measuring dissimilarities that overcome some flaws in existing measures.

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What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures

This manual has a twofold aim: to present the WeightedCluster library and offer a step-by-step guide to creating typologies of sequences for the social sciences. In particular, this library makes it possible to represent graphically the results of a hierarchical cluster analysis, to group identical sequences in order to analyse a larger number of sequences, to compute a set of measures of partition quality and also an optimized PAM (Partitioning Around Medoids) algorithm taking account of weightings. The library also offers procedures to facilitate the choice of a particular clustering solution and to choose the optimal number of groups. In addition to the methods, we also discuss the building of typologies of sequences in the social sciences and the assumptions underlying this operation. In particular we clarify the place that should be given to the creation of typologies in the analysis of sequences. We thus show that these methods offer an important descriptive point of view on sequences by bringing to light recurrent patterns. However, they should not be used in a confirmatory analysis, since they can point to misleading conclusions.

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WeightedCluster Library Manual A practical guide to creating typologies of trajectories in the social sciences with R

In this article we define a methodological framework for analyzing the relationship between state sequences and covariates. Inspired by the ANOVA principles, our approach looks at how the covariates explain the discrepancy of the sequences. We use the pairwise dissimilarities between sequences to determine the discrepancy which makes it then possible to develop a series of statistical-significancebased analysis tools. We introduce generalized simple and multi-factor discrepancy-based methods to test for dierences between groups, a pseudo R 2 for measuring the strength of sequence‐covariate associations, a generalized Levene statistic for testing dierences in the within-group discrepancies, as well as tools and plots for studying the evolution of the dierences along the timeframe and a regression tree method for discovering the most significant discriminant covariates and their interactions. In addition, we extend all methods to account for case weights. The scope of the proposed methodological framework is illustrated using a real-world sequence dataset.

/pdf/discrepancy-analysis-of-state-sequences-1duhftaeg5.pdf

Discrepancy Analysis of State Sequences

This paper is concerned with the summarization of a set of categorical sequences. More specifically, the problem studied is the determination of the smallest possible number of representative sequences that ensure a given coverage of the whole set, i.e. that have together a given percentage of sequences in their neighbourhood. The proposed heuristic for extracting the representative subset requires as main arguments a pairwise distance matrix, a representativeness criterion and a distance threshold under which two sequences are considered as redundant or, identically, in the neighborhood of each other. It first builds a list of candidates using a representativeness score and then eliminates redundancy. We propose also a visualization tool for rendering the results and quality measures for evaluating them. The proposed tools have been implemented in our TraMineR R package for mining and visualizing sequence data and we demonstrate their efficiency on a real world example from social sciences. The methods are nonetheless by no way limited to social science data and should prove useful in many other domains.

/pdf/extracting-and-rendering-representative-sequences-1tikgnncfb.pdf

Extracting and Rendering Representative Sequences

We explore how recent data mining-based tools developed in domains such as biomedicine or text mining for extracting interesting knowledge from sequence data could be applied to personal life course data. We focus on two types of approaches: 'survival' trees that attempt to partition the data into homogeneous groups regarding their survival characteristics, i.e., the duration until a given event occurs and the mining of typical discriminating episodes. We show how these approaches may fruitfully complement the outcome of more classical event history analyses and single out some specific issues raised by their application to socio-demographic data.

/pdf/mining-event-histories-a-social-science-perspective-3fwgsqmq2e.pdf

Mining event histories: a social science perspective

In this article we consider objects for which we have a matrix of dissimilarities and we are interested in their links with covariates. We focus on state sequences for which pairwise dissimilarities are given for instance by edit distances. The methods discussed apply however to any kind of objects and measures of dissimilarities. We start with a generalization of the analysis of variance (ANOVA) to assess the link of complex objects (e.g. sequences) with a given categorical variable. The trick is to show that discrepancy among objects can be derived from the sole pairwise dissimilarities, which permits then to identify factors that most reduce this discrepancy.We present a general statistical test and introduce an original way of rendering the results for state sequences. We then generalize the method to the case with more than one factor and discuss its advantages and limitations especially regarding interpretation. Finally, we introduce a new tree method for analyzing discrepancy of complex objects that exploits the former test as splitting criterion. We demonstrate the scope of the methods presented through a study of the factors that most discriminate Swiss occupational trajectories. All methods presented are freely accessible in our TraMineR package for the R statistical environment.

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Nicolas S. Müller

Papers

Analyzing and Visualizing State Sequences in R with TraMineR

Discrepancy Analysis of State Sequences

Extracting and Rendering Representative Sequences

Mining event histories: a social science perspective

Discrepancy Analysis of Complex Objects Using Dissimilarities