Thanks for your comment, Steve!

I checked my DV's normality and it has normal distribution.
And I simply transform for easy of interpretation.
Here is the distribution from stata;

         Trust |      Freq.     Percent        Cum.
------------+-----------------------------------
               0 |      1,591        5.15        5.15
   .1111111 |      1,635        5.29       10.44
   .2222222 |      2,616        8.47       18.91
   .3333333 |      5,604       18.14       37.04
   .4444444 |      5,270       17.06       54.10
   .5555556 |      4,712       15.25       69.35
   .6666667 |      5,250       16.99       86.34
   .7777778 |      2,143        6.94       93.28
   .8888889 |        984        3.18       96.46
              1 |      1,093        3.54      100.00
------------+-----------------------------------
         Total |     30,898      100.00

As I combined 3 indicators that has 4 categories to make this DV,
it is also not binomial distribution, either.

Thanks for your further comment in advance.
-Changkuk


On Mon, Mar 29, 2010 at 9:01 AM, Steven J. Pierce <pierces1@msu.edu> wrote:

> Since your DV is a proportion, it may not really be normally distributed in
> it's original form. Perhaps you should transform the dependent variable
> before running the analysis. Using an arcsine square root transformation
> may
> help (you should be able to find details via Google).
>
>
> Steven J. Pierce
> Associate Director
> Center for Statistical Training & Consulting (CSTAT)
> Michigan State University
> 178 Giltner Hall
> East Lansing, MI 48824
> Web: http://www.cstat.msu.edu
>
>
> -----Original Message-----
> From: jungck [mailto:jungck@gmail.com]
> Sent: Sunday, March 28, 2010 9:36 PM
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Interaction Terms controlling Influence case in lmer
>
> 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.
>
> lmerPlotInt.fnc(model.after.control.influence, "x", "y", "y:x",
> which="matplot", ylabel="DV", ylim=c(0,1.1))
>
> The issue here seems only to appear when I control influence country by
> using the package influence.ME. (Actually there is another issue involved
> this package like I couldn't plot after using "exclude.influence" function.
> In R, it only makes coordinates without any lines)
>
> When I include influence case(s), I can see those lines within the range of
> DV.
> Can anyone familiar with lmerPlotInt.fnc let me know what's going on?
>
> 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.
>
> -Chang
>
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>
>
>
>

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