[R-sig-ME] Modeling attacks and victories

Thierry Onkelinx thierry.onkelinx at inbo.be
Thu Apr 20 09:34:16 CEST 2017

Dear Paul,

I'd focus on two different points first:
a) what does the student wants to model: the probability of success? the
number of events? the number of successful events? something else?
b) what are the statistical skills of the student. That will determine the
appropriate level of statistical machismo.

Best regards,


ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2017-04-19 17:39 GMT+02:00 Paul Johnson <pauljohn32 op gmail.com>:

> Could I ask for pointers on how to guide a student in my multilevel
> modeling course?
> The outcome data is terrorist attack events, with one row per event
> (events are listed by country and year). The data also indicates if
> each attach is a "success" (I have no idea how that's measured, if it
> matters I can find out).
> The student says that, in his field, what they would do is aggregate
> events at the country/year level to create a "proportion of successful
> attacks" variable. If a country has no events, then it is scored as a
> 0.  Then they'd run random intercept models using country as case
> identifier, possibly with other country level predictors that vary
> across time.
> I think we can do better than that. The number of events within
> countries varies widely, some have 0 or 1 attack, while in some years
> there are 30 or more.  Measuring the proportions is, obviously,
> sensitive to the number in the denominator.  Some countries are scored
> on a scale 0, .5, 1, while others are scored as 0, 0.03, 0.06, and so
> forth.  Other obvious problems are the presence of 0's.
> My first idea was to made this a binomial glm and predict successes as
> a proportion of attacks.  That's a problem because there are lots of 0
> attack country/years, but also because I'm
> It looks to me like we need to explore this as a two part model, where
> part 1 predicts (attacks > 0) and part 2 is binomial among the
> countries and places where attacks > 0. I'm not finding discussion of
> this particular example while searching (I probably don't know the
> magic words).  However, we need to insert the country-level intercept
> in both models, and perhaps the country effect needs to be correlated
> between the two models.
> pj
> --
> Paul E. Johnson   http://pj.freefaculty.org
> Director, Center for Research Methods and Data Analysis
> http://crmda.ku.edu
> To write to me directly, please address me at pauljohn at ku.edu.
> _______________________________________________
> R-sig-mixed-models op r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list