glmmTMB: testing for temporal variation in effect of fixed predictor on response variable

Dear Brenna,

Please keep the mailing list in cc.

(1 + fixed|year) fits a random intercept and a random slope along "fixed"
for every "year". Keep in mind that you need enough data to support such a
model. See e.g.
https://www.muscardinus.be/2018/02/highly-correlated-random-effects/

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
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
///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


Op wo 7 nov. 2018 om 14:18 schreef Brenna Levine <levine.brenna.a at gmail.com

:

Hi Thierry,

Thanks for your detailed response. I have one fixed effect that is not
discrete. How should I fit this interaction in this case?

Thanks!

Brenna

On Wed, Nov 7, 2018, 3:35 AM Thierry Onkelinx <thierry.onkelinx at inbo.be
wrote:

Dear Brenna,

Adding a random effect (1|year:fixed) makes sense, assuming that both
year and fixed are discrete. Note that adding this allows for a very
liberal temporal variantion by the fixed effect. Each level of the
interaction is independent from all other levels.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be


///////////////////////////////////////////////////////////////////////////////////////////
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

///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


Op wo 7 nov. 2018 om 00:16 schreef Brenna Levine <
levine.brenna.a at gmail.com>:

Dr. Bolker,

I'm hoping that I might be able to bother you with a quick question. I am
trying to *test whether there is significant temporal variation in the
effect of a fixed predictor on my response variable *with a model in
which
year is specified as a random effect (I have 20 years of data).
Currently,
I am doing this by fitting an interaction between the fixed effect and
the
random effect of year as* (1|year:fixed)* (per a recommendation that I
saw
on RSeek.org at some point and some tips that I have read on this
list-serve), and am testing the significance of this random interaction
with a LRT (i.e., with a model lacking this interaction).

Could you tell me if (a) (1|year:fixed) is the correct way to specify
this,
and (b) if not, do you have a recommendation for how I should specify
this
interaction to test for temporal variation in the effect of a fixed
predictor on my response variable?

Thanks.

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