1. I think you are right. You did ask for "adjusted means", not "adjusted
slopes".  :-)

2. For graphing the slopes, I prefer to create a new data.frame with a
suitable seq(from= , to=, length= ) for the continuous predictor and
appropriate levels of the categorical predictors, then use the
predict(fittedmodel, newdata= ) function, and use the resulting predicted
values and new data.frame for plotting.

kevin



On Wed, Oct 16, 2013 at 9:13 AM, Catherine Bois <C.Bois@sms.ed.ac.uk> wrote:

> Thanks guys. I am using lsmeans, however the problem is that I am
> interested in a categorical:continuous predictor interaction so I have two
> problems
>
> 1. lsmeans seems to take one value of the continuous predictor and compare
> the categorical predictors in relation to the outcome variable on this one
> value, as opposed to comparing slopes of the variable; I therefore wanted
> to graph this interaction somehow with the adjusted means
>
> 2. This means that I would need to somehow extract all the model adjusted
> values for these interaction terms and "build my own graph", outside of
> lsmeans capabilities, if that makes sense.
>
> Sorry if this sounds unclear!
>
>
> Quoting Douglas Bates <bates@stat.wisc.edu> on Wed, 16 Oct 2013 07:54:16
> -0500:
>
>  On Wed, Oct 16, 2013 at 4:13 AM, Catherine Bois <C.Bois@sms.ed.ac.uk>
>> wrote:
>>
>>  Hi,
>>>
>>> I am currently using nlme to fit a mixed model. However, I was wondering
>>> if there is a function to extract model adjusted means derived from an
>>> nlme
>>> object, in order to subsequently plot in another programme?
>>>
>>>
>> I can't answer your question because I don't know what adjusted means are.
>>  I am cc:ing the R-SIG-Mixed-Models@R-project.**org mailing list on this
>> reply in case someone reading that list can help you.
>>
>>
>
>
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-- 
Kevin Wright

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