models - each user can have up to 8 rows/days of data (but fewer if they weren't active on all 8 days) - see [[220518_170142 campaign 3 daily analysis and results|here]] for distribution of effects for each day ```r # summed and count models # SE cluster on block & day feglm(t1 ~ conditionC * t0LC | block + day, dt1, family = "quasipoisson") # HC1 feglm(t1 ~ conditionC * t0LC | block + day, dt1, family = "quasipoisson", vcov = "HC1") # fraction models # SE cluster on block & day feols(t1 ~ conditionC * t0LC | block + day, dt1) # HC1 feols(t1 ~ conditionC * t0LC | block + day, dt1, vcov = "HC1") ``` summary - effects are much stronger than [[220417_110655 cluster block vs hc1|original analysis]] - HC1 SE (cyan in figures below) is better than clustering on block **and** day (red below) (previously we only had 1 fixed effect [block] and clustered SEs only on that) - clustering on block was better in original analysis # summed badness ![[_sum_winsorize-0.95-clustse 2.png]] ![[_sum_winsorize-0.85-clustse 2.png]] # count badness ![[_count_winsorize-0.85-clustse 3.png]] ![[_count_winsorize-0.95-clustse 1.png]] # fraction badness ![[_frac_winsorize-0.95-clustse 3.png]] ![[_frac_winsorize-0.85-clustse 2.png]]