[R-sig-ME] Help with nested and crossed effects in models with 2- and 3-way interactions

[Michal Kahn]

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>

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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