Thanks for your response!
I would try with coef and curve command in R.
But as I still working on R program, can you let me know a bit more detail
about what should be done?

Here is what I did;

model.after.control.influence<-lmer(DV~x1+x2+x3+(1+x1+x2|country)+y1+y2+y3+x1:y1+x2:y1+x1:y2+x2:y2,
data, REML=F)
display(model.after.contro.influence, dig=3, detail=T)

After controling influence(s), I can always see the lines appearing above or
just above 1 that is max of my DV.
As my models have shown appropriate range, I assume there might be some
issue related with influence case(s)
that affect the plot/lines or incompatibility of two packages between
languageR and influence.ME.

Any help will be appreciated.



On Mon, Mar 29, 2010 at 8:13 AM, Andy Fugard (Work)
<andy.fugard@sbg.ac.at>wrote:

> jungck wrote:
> > Dear all,
> >
> > I am working on interaction terms between two levels.
> > But when I plot them with the package languageR and following function,
> > I could have "impossible" predicted values, which is going beyond
> > my dependent variable.
> > My DV ranges 0 to 1 in continuous variable. But lines appear above 1.
>
> How did you fit the model?  It would be useful to see the call you made
> to lmer.  The plotting problems may be due to you having fitted an
> inappropriate model, rather than pointing to an error in the plotting
> function.
>
>

> >
> > And, I wonder if there is another alternative package/function for
> plotting
> > interaction terms in mixed effect model using LMER or others.
> > Thanks much in advance.
>
> You can do it by hand.
>
> Just extract the model coefficients using "coef" and then write a
> formula represented by the model.  The "curve" function can be useful
> for this.
>
> best wishes,
>
> Andy
>
> --
> Andy Fugard, Postdoctoral researcher, ESF LogICCC project
> "Modeling human inference within the framework of probability logic"
> Department of Psychology, University of Salzburg, Austria
> http://www.andyfugard.info
>

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