[R-sig-ME] Random slope with cross-level interaction

[Salahadin Lotfi]

I am not sure this is what you are asking, but did you try *summary**( your
model) *function? It prints out the main effect coefficients (and more
info) that you are looking for.
Also, run the library(lmerTest) to get a f-like table of type III sum of
the squares using *anova (your model) *function.
I would recommend going through this lmer vignette as well.
https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf



On Sun, Jul 19, 2020 at 1:35 PM Ben Bolker <bbolker using gmail.com> wrote:

>    Assuming that fertilizer is a numeric covariate and wheatlanduse is
> categorical, you might want
>
> lmer( Production ~ seed + fertilizer *wheatlanduse + (1 + fertilizer |
> Household) , contrasts=list(wheatlanduse=contr.sum), ...)
>
>    that would mean that the main effect for fertilizer would represent
> the *average* effect across levels of wheatlanduse.  The estimated main
> effect of wheatlanduse will represent the expected differences across
> levels at zero fertilizer (or, if you center fertilizer by subtracting
> the mean, at the average fertilizer level)
>
>     I'm not sure what you mean by "cross-level interaction" ...
>
>
>
>
> On 7/19/20 12:59 PM, Yashree Mehta wrote:
> > Hi Salahadin, Thanks for your reply. It is very helpful. Then, is
> > there a way I can extract the coefficient estimate of the fixed effect
> > of fertilizer as well as that of wheatlanduse as a main effect?
> > Regards, Yashree On Sun, Jul 19, 2020 at 6:59 PM Yashree Mehta
> > <yashree19 using gmail.com> wrote:
> >> Hi Salahadin, Thanks for your reply. It is very helpful. Then, is
> >> there a way I can extract the coefficient estimate of the fixed
> >> effect of fertilizer as well as that of wheatlanduse as a main
> >> effect? Regards, Yashree On Sun, Jul 19, 2020 at 5:46 PM Salahadin
> >> Lotfi <salahadin.lotfi using gmail.com> wrote:
> >>> Hi Yashree, The interpretation of the interaction term do change
> >>> whether you include the main effect of not. Usually having only the
> >>> interaction term in the model requires a specific hypothesis. Thus,
> >>> the lower level terms (i.e., the main effects) are almost always
> >>> included. The lmer function automatically includes the lower level
> >>> terms even if you just include an interaction term. For example, if
> >>> you setup your model as follow, the main effects of fertilizer and
> >>> wheatlanduse will be still taken into account. Production = seed +
> >>> fertilizer : wheatlanduse + (1 + fertilizer | Household) Thanks,
> >>> Sala ************* Salahadin (Sala) Lotfi PhD Candidate of Cognitive
> >>> Psychology & Neuroscience University of Wisconsin-Milwaukee Anxiety
> >>> Disorders Laboratory President, Association of Clinical and
> >>> Cognitive Neuroscience, UWM On Sun, Jul 19, 2020 at 5:02 AM Yashree
> >>> Mehta <yashree19 using gmail.com> wrote:
> >>>> Hi, I have the following model: Production = seed + fertilizer +
> >>>> fertilizer : wheatlanduse + (1 + fertilizer | Household) As the
> >>>> formula indicates, the household level is specified as the random
> >>>> intercept. Fertilizer is specified as random slope , and has also
> >>>> been specified as a fixed effect. I am interested in cross-level
> >>>> interaction between fertilizer and the wheatlanduse variable. So, I
> >>>> have inserted "fertilizer : wheatlanduse". My question is: Do I
> >>>> have to include "wheatlanduse" as a main effect in the formula as
> >>>> well? Or is it acceptable to only have it as a part of the
> >>>> interaction term? Thank you, Regards, Yashree [[alternative HTML
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