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I'm running a glmer and have a few questions regarding how to interpret the output and how to report it:

it's not clear to me what the main effects are given that they are all in reference to a base category, so probably it is useless to talk about a 'main effect' of X with an associated p-value.

I would like to run further contrasts to the ones report it in the summary output, can I use emmeans()?

If I get the p-values that I'm interested in with emmeans, should I report these p-values together with the associated estimate? and how should I report this estimate? is it a Beta?

This is my code:

prob_stat = glmer(cbind(freq_correct, count-freq_correct) ~ 0+impulsivity*ITI +(1|rat_ID), family ="binomial", data= day_1) summary(prob_stat)

MainContrasts<- emmeans(prob_stat, pairwise~ ITI | impulsivity)

enter image description here

Ale
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Chiara Toschi
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  • related to this: https://stats.stackexchange.com/questions/87412/how-to-interpret-2-way-and-3-way-interaction-in-lmer/478377#478377 – Chiara Toschi Jul 22 '20 at 14:37
  • Please don't put picture of output into your posts. Just post the actual output with ``` above and below and it will be formatted nicely – Robert Long Jul 22 '20 at 16:14
  • Also why doesn't you model have a global intercept ? And please explain your study design and research question(s) – Robert Long Jul 22 '20 at 16:22
  • Thank you! it doesn't have an intercept because I specified 0 in the coding, if you remove that impulsivityHI becomes the intercept. **research question and design**: I have 36 rats some are highly impulsive (HI), some are low-impulsive (LI) and some are MIDs. I ran them on two separate days on a task that presents a cue after 4 variable inter-trial intervals (ITIs), these intervals are: 3000ms, 5000ms, 7000ms and 9000ms. I'm looking at the probability of making a correct response (DV) as a function of the impulsivity phenotype (3 levels); Day (2 levels) and the ITI (4 levels). Thank you! – Chiara Toschi Jul 22 '20 at 20:47

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