On 26Aug 2020, at 16:57, Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
Dear Mollie,
I agree that (1| DayOfWeek:date) doesn't make sense and it is better to use (1|Date). IMHO it might be sensible to include DayOfWeek in the model. (1|DayOfWeek) + (1|Date) or DayOfWeek + (1|Date). So either as random effect or as fixed effect. Having a factor both as fixed and random intercept is nonsense. Given there are only 7 days in week, I'd use DayOfWeek rather as a fixed effect.
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 <mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be <http://www.inbo.be/>
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Op wo 26 aug. 2020 om 16:16 schreef Mollie Brooks <mollieebrooks at gmail.com <mailto:mollieebrooks at gmail.com>>:
I?m guessing that the problem with mod3 could be another instance of confusion with nested effects.
As originally written, the random effects in mod3 are
(1|DayOfWeek/date) = (1|DayOfWeek) + (1| DayOfWeek:date)
The second term doesn?t make sense to me when each date can only be accompanied by either 0 or 1 for DayOfWeek.
Maybe you want (1|Subject) + (1|Date) in mod3. That model could address both hypotheses.
cheers,
Mollie
On 23Aug 2020, at 12:50, Michal Kahn <michalkahn10 at gmail.com <mailto:michalkahn10 at gmail.com>> wrote:
Hello there! I am running a mixed model in lmer, testing the effects of
Covid restrictions on sleep, comparing 2 cohorts of individuals- one from
2019 and one from 2020, coded 0/1 (between subjects). Each individual was
measured repeatedly for ~130 consecutive nights, and each row in the
dataset represents a single night. I also have a binary Lockdown IV, where
each night is coded 0/1 to indicate if it was before/after restrictions
were imposed in 2020 (and the equivalent dates for 2019). Finally, I have a
DayOfWeek IV, where each night is coded 0/1 to indicate if it represents a
weekday/weekend night. The simplified dataset looks something like:
[image: enter image description here] <https://i.stack.imgur.com/Ouuhw.png <https://i.stack.imgur.com/Ouuhw.png>>
My hypotheses are: (1) there will be a Cohort by Lockdown interaction
effect on sleep; and (2) there will be a Cohort by Lockdown by DayOfWeek
interaction effect on sleep.
For hypothesis 1, I ran:
mod1<- lmer(sleep ~ Cohort*Lockdown + (1|Subject) + (1|Date), data = COVID,
REML=FALSE)
Results seem reasonable, but I think I am not accounting for random slopes.
I have tried to model the slopes as follows, but the model failed to
converge.
mod2<- lmer(sleep ~ Cohort*Lockdown + (Lockdown|Subject), data = COVID,
REML=FALSE)
As for the 2nd hypothesis, if I understand correctly, nights are nested
within DayOfWeek, which are crossed with Lockdown (since each level of
Lockdown includes both weekdays and weekends). I tried the following code,
but am getting a singular fit warning (boundary (singular) fit: see
?isSingular)
mod3<- lmer(sleep ~ Cohort * Lockdown * DayOfWeek + (1|DayOfWeek/date),
data = COVID, REML=FALSE)
Could anyone direct me as to what should be changed in these models? Many
thanks in advance for your help!
Mika
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