Hello everyone,

Thank you kindly to everyone who replied.  Ista's comment was the one 
that had exactly what I needed- though it did take some tinkering.  For 
the benefit of the next person who comes along, I'm adding the changes I 
needed to make to Ista's code:

Running the code Ista initially provided, the following error in bold 
comes up


> data(cake, package="lme4")
> cake$a2 <- ifelse(cake$angle < mean(cake$angle, na.rm=TRUE), 0, 1)
>
> fm1 <- lmer(a2 ~ recipe * temp + (1|recipe:replicate),
+             data = cake,
+             family = "binomial",
+             REML = FALSE)
>
> library(effects)
> plot(effect("recipe:temp", fm1), grid=TRUE)
*Error in plot(effect("recipe:temp", fm1), grid = TRUE) :**
**   error in evaluating the argument 'x' in selecting a method for function 'plot': Error: $ operator not defined for this S4 class**
*

The solution to this is to follow on to the next line in the example in 
the effects display from John Fox's JSS paper 
http://www.jstatsoft.org/v08/i15:

detach(package:lme4) # if previously attached
library(nlme)
data(cake, package="lme4")

cake$rep <- with(cake, paste( as.character(recipe), as.character(replicate), sep=""))
fm2 <- lme(angle ~ recipe * temperature, data=cake,
            random = ~ 1 | rep, method="ML")
plot(effect("recipe", "temperature", fm2), grid=TRUE)

However, this produces a heck of a lot of panels, which are kind of 
difficult to interpret.... so we force everything onto one graph- which 
is accomplished by adding the following line (also from John Fox's 
working paper (pg 14): (note, I haven't updated this to reflect the cake 
example

> plot(effect("recipeism*extraversion", cowles.mod,
+
xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6))),
+
multiline=TRUE, ylab="Probability(Volunteer)")

You all have been a great help.  I hope this summary e-mail helps others 
in the future.

Shane


**
On 01/28/2013 05:13 PM, Ista Zahn wrote:
> Hi Shane,
>
> Take a look at the effects package, I believe it has what you are
> looking for. Here is a modified example from ?effect
>
> library(lme4)
>
> data(cake, package="lme4")
> cake$a2 <- ifelse(cake$angle < mean(cake$angle, na.rm=TRUE), 0, 1)
>
> fm1 <- lmer(a2 ~ recipe * temp + (1|recipe:replicate),
>              data = cake,
>              family = "binomial",
>              REML = FALSE)
>
> library(effects)
> plot(effect("recipe:temp", fm1), grid=TRUE)
>
>
> HTH,
> Ista
>
> On Mon, Jan 28, 2013 at 5:32 PM, Shane Gleason <sgleason@siu.edu> wrote:
>> Hello everyone,
>>
>> I am working with lme4, specifically the glmer command.  In a multilevel
>> model I have an interaction term which is significant. However, with my
>> discipline we stress that an interaction term cannot be evaluated by p value
>> alone and one must reference a graphical display of the interaction, with
>> confidence intervals drawn around it.
>>
>> That is to say if the interaction is X*Z then the interaction might be
>> significant when X=1 and Z=3, but may not be significant if X=4 and Z=9.
>>
>> I have searched around and I can't seem to find any indication of how to
>> graph out the interaction (though I see some references to it for lmer (but
>> never glmer)).  So, my question is two-part- 1: Does this assumption about
>> how interaction terms work hold for MLMs?  2: If the answer to 1 is yes,
>> does anyone know of software I can use to graph out these interactions?
>>
>> Thanks,
>>
>> Shane
>>
>> --
>> Shane Gleason
>> Doctoral Candidate
>> Southern Illinois University:Carbondale
>> Department of Political Science
>> Faner 3172
>>
>> _______________________________________________
>> R-sig-mixed-models@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

-- 
Shane Gleason
Doctoral Candidate
Southern Illinois University:Carbondale
Department of Political Science
Faner 3172


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