models - fixed effect block & day - compare cluster SEs on block vs user - .85 winsorization seems to be the best in this dataset (most significant effects) and no winsorization is the worst (no significant results at all) - **but initial analyses on the bigger 80k dataset suggest higher thresholds might be better in that dataset?** how much to winsorize might depend on dataset... - clustering SE on user might be a little better than clustering on block, but not consistently the case - effects are stronger than [[220417_110655 cluster block vs hc1|original analysis]] (without day fixed effects) - there are significant effects even for fraction badness in this analysis, whereas original analyses there weren't any significant effects for [[220417_110655 cluster block vs hc1#fraction bad retweets|fraction badness]] ```r # summed vs count model: quasipoisson feglm(t1 ~ conditionC * t0LC | block + day, dt1, family = "quasipoisson", cluster = ~ block) feglm(t1 ~ conditionC * t0LC | block + day, dt1, family = "quasipoisson", cluster = ~ user) # fraction model: ols feols(t1 ~ conditionC * t0LC | block + day, dt1, cluster = ~ block) feols(t1 ~ conditionC * t0LC | block + day, dt1, cluster = ~ user) ``` ## winsorize at 0.85 and 0.95 - [[220531_173501 winsorize 90 and 99|see here for winsorizing at 0.90 and 0.99]] ## summed badness ![[_sum_winsorize-0.85-clustse 3.png]] ![[_sum_winsorize-0.95-clustse 3.png]] ## count badness ![[_count_winsorize-0.85-clustse 1.png]] ![[_count_winsorize-0.95-clustse 3.png]] ## fraction badness ![[_frac_winsorize-0.85-clustse 3.png]] ![[_frac_winsorize-0.95-clustse 2.png]]