[R-sig-ME] Opinions on model structure: fixed and random effects
thierry.onkelinx at inbo.be
Wed Jan 20 12:33:47 CET 2016
Some quick comments.
- Given that you want to predict, I would add year both the fixed and the
- Maybe you want to add some random slope to the strata (e.g. year or
- Maybe season fixed and season:year and/or season:strata as random.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
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
2016-01-19 1:59 GMT+01:00 Andrew Allyn <andrew.allyn op gmail.com>:
> Dear mixed model experts,
> I am hoping to get opinions on a model structure, and specifically,
> whether Year, Season and Strata variables should be included as random
> effects, fixed effects, or both?
> In a nutshell, I am building a species distribution model using 30+
> years of fisheries trawl data with the main objective of using the model
> to predict fish distributions under future climate scenarios and the
> secondary objective of evaluating the relative importance of temperature
> predictor variables compared to static, landscape variables (e.g.,
> depth, bottom type). The unit of observation is a trawl tow, which has
> an associated date (year, month, day), season (fall or spring) and
> strata (spatial identifier, where strata is a unique region based on
> biophysical characteristics and used for stratified random sampling
> purposes). Within our dataset we have multiple tows from the same strata
> within the same season and year. We will likely examine a few different
> frameworks (e.g., GLMM, GAMM, Boosted Regression Trees, Random Forests).
> Taking the GLMM as an example, my plan is to do the following:
> 1) Include YEAR as a random effect. Although we are somewhat interested
> in the variability among all years, we are not specifically interested
> in completing year to year comparison between all years. However, if we
> were, it sounds like an interesting approach would be to include year as
> both a random and fixed effect, which would allow us to look at
> variability among years (random component) as well as trend and change
> over years (fixed component).
> 2) Include STRATA as a random effect. Strata, in many ways, is similar
> to the idea of a plot in a traditional plot-based or split-plot sampling
> design. Including it as a random effect accounts for the fact that
> multiple samples from the same strata are not truly independent.
> Additionally, we are not explicitly interested in comparing among
> strata. Therefore, including it as a random effect makes the most sense.
> 3) Include SEASON as a fixed effect. With only two options, it does not
> make sense to include season as a random effect. Additionally, we are
> interested in seasonal differences. On a related note, what if you had a
> temperature variable measured at a seasonal scale (i.e., spring or fall
> mean temp)? Would you drop season as a factor in the hopes that the
> seasonal variability was captured by the temperature variable?
> Does this approach make sense?
> Thank you in advance for your time and insight.
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