Dear Elizabeth,

I think the "effects" package is what you are looking for.

Best,
Tobias


On 22 Oct 2013, at 23:11, Elizabeth Graham <sharon.graham@pg.canterbury.ac.nz> wrote:

> Yes, good point, thank you!  That was explained to me by someone else as well, who recommended that I make my plots based on the coefficients of the model instead... do you, or anyone else reading this, know how to do that?  I think I will need to somehow combine or group coefficients by experimental level, for example, to make a Treatment-Shade interaction plot I will need a coefficient value for treatment-shaded, treatment-unshaded, control-shaded, and control-unshaded. 
> 
> Thanks again for your input, much appreciated.
> 
> Elizabeth
> ________________________________________
> From: r-sig-mixed-models-bounces@r-project.org [r-sig-mixed-models-bounces@r-project.org] on behalf of S Ellison [S.Ellison@lgcgroup.com]
> Sent: Wednesday, October 23, 2013 2:25 AM
> To: r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] Error structure for split-split plot with treatment across blocks
> 
>> -----Original Message-----
>> From: Elizabeth Graham [mailto:sharon.graham@pg.canterbury.ac.nz]
>> Sent: 21 October 2013 20:27
>> m2<-lme(log(density)~Treatment*Shade*Leaves,
>>                        random=~1|Stream/Shade/Leaves)
>> 
>> was actually my original model structure, but has the same incongruity
>> between model results and interaction plots - try
>> interaction.plot(Shade, Treatment, density) for an example - the
>> treatment and control lines do not intersect at all, and standard error
>> bars do not overlap.
> 
> The default interaction.plot doesn't use the Stream random effect in its error bars; it's using the group means and standard errors based on assumed simple independent replication. Those 'replicates' aren't independent. That will tend to give underestimated standard errors compared to a correct nested random effects structure, as the Stream effect is quite big.
> 
> S Ellison
> 
> 
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