[R-sig-ME] Multiple contrasts and what data to include when writing up a three-way interaction in lmer?

[Tove Jansson]

Tim - Thanks for the suggestions. It’s not completely clear if visreg() will work with lmer (lack of a “predict” function?), though there seems to be some discussion at the end of the associated paper about what it can do with such models. I will definitely look into it. I believe I tried plotLMER, but had trouble getting the code straight. I will look again.

Any suggestions regarding including simple effects without necessitating further adjustment for multiple comparisons?

Thanks 


> On Feb 12, 2015, at 5:25 PM, Tim Meehan <tmeeha at gmail.com> wrote:
> 
> Hi Tove,
> 
> The visreg package [visreg()] and languageR package [plotLMER.fnc()] can sometimes be helpful for wrapping your head around interactions.
> 
> Best,
> Tim
> 
> On Thu, Feb 12, 2015 at 4:09 AM, Tove Jansson <tove.jansson at gmail.com <mailto:tove.jansson at gmail.com>> wrote:
> Hi,
> 
> I have a question about three-way (or any) interaction data. Perhaps my question is more conceptual than allowed on this list - but I hope it can still be considered relevant - I am very new to mixed models and it has been difficult for me to find an answer for questions regarding interactions with continuous predictors.
> 
> Brief background:
> 
> Our lmer model has a significant three-way interaction. Our DV is continuous and we have three predictors. One predictor is categorical (three levels) and the other two predictors are continuous.
> 
> To view all possible contrasts within the highest order interaction we have been using a custom contrast matrix, in a similar way as to what was done here: http://stats.stackexchange.com/questions/43664/mixed-model-multiple-comparisons-for-interaction-between-continuous-and-categori <http://stats.stackexchange.com/questions/43664/mixed-model-multiple-comparisons-for-interaction-between-continuous-and-categori> <http://stats.stackexchange.com/questions/43664/mixed-model-multiple-comparisons-for-interaction-between-continuous-and-categori <http://stats.stackexchange.com/questions/43664/mixed-model-multiple-comparisons-for-interaction-between-continuous-and-categori>>
> 
> To correct our alpha correction for multiple comparisons, we have been using the default single-step adjustment in the glht function in multcomp package.
> 
> The problem:
> 
> To minimize the adjustment for multiple comparisons, I limited my glht contrasts to the highest order interaction (the three-way).
> 
> But in order to understand the interaction, I need to also look at the simple effects for the categorical predictor. For example, it’s important for me to know if there was a significant difference at the original intercept value of a certain predictor so that I can know if an effect is a) emerging on account of an interaction or b) becoming stronger on account of the same interaction. However, if I include these simple effects in the contrasts, then I am forced to make adjustments to the alpha.
> 
> How is this typically handled? Is there a way to present the simple effects without further penalty to my p values? Or am I thinking about this the wrong way - does the mere fact that I want to look at them mean that I must correct my alpha more? Apologies if this should be perfectly obvious.
> 
> Also - can anyone recommend a good way of plotting three-way interactions for lmer output? I have been using plot() and effect() to create trellis plots, as seen below, but I find it difficult and confusing trying to control the parameters. For example, I have not found a way to manually select the values for Pred2 (incremental, select values are selected by default for the trellis plots) and I have not found a way to indicate confidence intervals. If anyone can direct me to a good source that explains how to use plot() with these sorts of objects, please do so. ggplot2 does not seem to work for effects objects.
> 
> Thanks in advance.
> 
> #plot multiline
> p0 <- effect(“condition:Pred1:Pred2", testTotalRegion2_lmer14)
> p1 <- plot(p0,
>         multiline=TRUE,
>         main="Region2",
>         xlab=“Pred1",
>         ylab="FD (logged + residualalized)",
>         key.args=list(x=1,y=1,corner=c(x=.8, y=1))
>         )
> p1
> 
> 
> 
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> 
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