Timeline for Interaction term in a linear mixed effect model in R
Current License: CC BY-SA 4.0
13 events
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| Jan 13, 2020 at 13:20 | comment | added | Stefan |
@Ben using the Anova() function (or mixed() function) just generates an ANOVA-type table which calculates the significance of the fixed effects in a lme-model. Same as if you ran a linear model on categorical predictors using the lm() function, c.f. m1 <- lm(weight~group, PlantGrowth); summary(m1), Anova(m1); and m2 <- aov(weight~group, PlantGrowth); summary(m2)
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| Jan 13, 2020 at 11:03 | comment | added | Ben | what is the meaning of using anova after fitting lmer? | |
| Dec 6, 2018 at 14:51 | history | edited | Stefan | CC BY-SA 4.0 |
Updated code from lsmeans syntax to emmeans syntax.
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| Dec 6, 2018 at 14:35 | comment | added | NickJ | A really nice, useful answer. As a minor addition it might be worth updating the answer to replace the lsmeans package with the new [link]cran.r-project.org/web/packages/emmeans/index.html[link] emmeans package (written by the same author(s) . | |
| Nov 16, 2018 at 19:31 | history | edited | Stefan | CC BY-SA 4.0 |
updated links
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| Nov 16, 2018 at 19:26 | history | edited | Stefan | CC BY-SA 4.0 |
updated links
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| Dec 24, 2015 at 4:56 | vote | accept | gsd | ||
| Dec 23, 2015 at 23:00 | comment | added | Stefan | Exactly, now you have it :) No need to thank me. If this answer helped you, you could consider accepting it. Again once the interaction is significant the most important step is visualization of the interaction. This will help you understand where the interaction is and what it means. Post-hoc tests are not always recommended. Also have a look here page 180ff. This a very good introduction into regression and ANOVA with R. | |
| Dec 23, 2015 at 22:41 | comment | added | gsd | From your answer, I get the impression that there is no need to interpret main effects in the presence of a significant interaction and I can follow up with the post-hoc tests if interaction is significant and main effects are not. is that correct? Thanks again for your detailed answer and telling me about the other useful packages! | |
| Dec 23, 2015 at 22:40 | comment | added | gsd | Regarding my question, the interaction and landuse are significant in my model. I know that interpreting main effects in the presence of an interaction can be misleading, so I was not sure how to interpret the main effects - by fitting another model that excludes interaction (ie model2) or by fitting models for each species seprately (ie model.sp1)? | |
| Dec 23, 2015 at 22:24 | comment | added | gsd | Thank you very much for your answer! I made a typing error in my syntax earlier which I have corrected now - ie replaced (1+landuse/site) with the correct version which is (1+landuse|site). Sorry about that! and I guess that is why you were confused about my fit. However, to answer your question, I am trying to fit landuse and species as fixed effects and sites as random effect with random slope and intercept. | |
| Dec 23, 2015 at 8:55 | history | edited | Stefan | CC BY-SA 3.0 |
deleted 5 characters in body
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| Dec 23, 2015 at 8:46 | history | answered | Stefan | CC BY-SA 3.0 |