Using the Relational Event Model (REM)



Organised by:

University of Manchester


Prof Mark Tranmer


Intermediate (some prior knowledge)


Claire Spencer, 0161 275 1980,


View in Google Maps  (M13 9PL)


Cathie Marsh Institute
Humanities Bridgeford Street
University of Manchester


The course will explain how the Relational Event Model (REM) may be used to investigate patterns in ordered or timed sequences of actions.

We begin by giving some examples for which ordered sequences of timed data may be collected, including patterns of behaviour of individuals over time, and interactions in a network of individuals over time.

We then introduce the REM in the context of other related methods, such as survival analysis, and also in the context of other ways of looking at sequences of actions, such as sequence analysis.

We explain the importance of taking into account the way in which the ordered or timed data were collected, and the actions that were observable at each point in the sequence, when analysing it, and explain how this can be achieved.

Next, we explain how an R package called informR can enable us to prepare ordered or timed data for analysis with a REM. Data preparation with InformR allows us to take into account the way in which the data were collected, and the actions that are observable at each point in the sequence. InformR also allows us to create “sequence statistics” to allow us to investigate particular patterns in the timed or ordered sequence of actions that may be of particular substantive interest.

Finally we explain how relevent, an R package, can be used to fit REMs to ordered or timed sequence data. We give examples, and explain how the results of a REM analysis that has been carried out using relevent can be interpreted.


  •  Introduce the Relational Event Model (REM) in the context of existing models and approaches. Explain the advantages of the REM over other methods, given particular substantive aims and data.
  • Explain the need to take into account the data collection mechanism in the analysis of ordered or timed sequences of actions.
  • Provide hands-on training in the use of informR (an R package) to prepare the ordered or timed sequence data for analysis with a REM. This includes setting up “sequence statistics”.
  • Provide hands-on training in the use of relevent (an R package) to fit Relational Event Models (REMs).
  • Understand how to interpret the results of such analyses.



Some familiarity with models for longitudinal data such as the Cox proportional hazards model as used in survival analysis would be advantageous, but is not absolutely essential. Familiarity with regression and logistic regression models is assumed. Knowledge of networks could be useful for some of the examples given in the later part of the course, but is not assumed.

The course will use the software R for the analysis, preparation and modelling of the data – this is a free download and available for both Mac and PC using a variety of operating systems. The practical sessions will include all necessary code for R, so that an all-round knowledge of R is not required. Some familiarity with R would however be useful, via for example: Quick-R: but is not absolutely essential.

Recommended Preparation:

We don’t assume you will read all the material below, but if you did at least look at some of it, it would significantly enhance your learning on the course.

Background theory:

Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology38(1), 155-200.

Discussion of the R packages informR and relevent:

(The reference below includes some real-data examples based on time use by older people.)

Marcum C.S. and Butts C.T. (2015) Constructing and Modifying Sequence Statistics for relevent Using informR in R. Journal of Statistical Software, Volume 64, Issue 5.

Example application:

(this paper is about sequences of events in networks of individuals; includes an empirical example)

Tranmer, M., Marcum, C. S., Morton, F. B., Croft, D. P., & de Kort, S. R. (2015) Using the relational event model (REM) to investigate the temporal dynamics of animal social networks. Animal Behaviour101, 99-105.

Quick r website:

R (R can be downloaded via here)

R studio: (A handy environment for running R).

Level: Advanced (specialised prior knowledge)



£30 per day for UK/EU registered students
£60 p/day staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff, staff at UK/EU registered charity organisations, recognised UK/EU research institutions
£220 per day for all other participants.

Website and registration:


North West


Longitudinal Research , Event History Analysis, Survival analysis, Time Series Analysis, R

Related publications and presentations:

Longitudinal Research
Event History Analysis
Survival analysis
Time Series Analysis

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