Works with lme(), as well.

Am 18.02.2014 21:14, schrieb Nick Isaac:
> Daniel,
>
> The r.squaredGLMM() function in MuMIn implements the method of Nakagawa
> & Schielzeth (2012) <http://doi.wiley.com/10.1111/j.2041-210x.2012.00261.x>,
> which seems like a sensible approach. It works well with mer() objects but
> I've not tried it with models fitted in lme().
>
> Best wishes, Nick
>
>
> On 18 February 2014 19:39, Douglas Bates <bates@stat.wisc.edu> wrote:
>
>> Generally it is best to send questions like this to the
>> R-SIG-Mixed-Models@R-project.org mailing list (see instructions at
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models), which I am
>> cc:ing on this reply.  Many of those who read that list can reply and often
>> much faster than I am able to.
>>
>>
>>
>> On Fri, Feb 14, 2014 at 4:43 PM, Daniel R Lammel <drlammel@gmail.com>
>> wrote:
>>
>>> Dear Prof. Bates,
>>>
>>> I'm soil ecologist and great fan of your "nlme" package for R.
>>> Congratulation/thanks for sharing all this knowledge for the academic
>>> community!!
>>>
>>> I found a "nlme" code for estimating the R2 of a linear mixed model (
>>> http://jonlefcheck.net/2013/03/13/r2-for-linear-mixed-effects-models )
>>> and saw that his colleague calculate the uncertainly about the
>>> calculations: ( http://mbjoseph.github.io/blog/2013/08/22/r2 ) .
>>>
>>> I'd like to ask you if you agree with such method, and if you could
>>> update the "nlme" package with these codes?
>>>
>> I don't think there is a good definition of R^2 for anything other than a
>> Gaussian linear model.  Even if I did think it was a good idea, I am no
>> longer the maintainer of the nlme package.
>>
>>
>>> Also another question. In linear regression we get a r2 and p-value:
>>> ā??Multiple R-squared: 0.7239, Adjusted R-squared: 0.6319
>>> F-statistic: 7.867 on 5 and 15 DF, p-value: 0.0008221ā?³
>>>
>>> Based on the r2 uncertainty (or other method) is it possible to
>>> estimate a p-value for the r2 of a mixed model? Is it possible to
>>> create an R code in "nlme" for this?
>>>
>> As readers of the list will know, I'm not a great fan of p-values either.
>>
>> A Gaussian linear model is a special case in which the mathematics all
>> works out well so that many summary statistics can be given meaningful
>> definitions.  Once you leave the comfortable world of such models these
>> concepts are no longer meaningful and I don't think it is a good practice
>> to cobble up definitions that sort-of, kind-of, in-a-way reflect some
>> property of a statistic like R^2 and say that this is how it generalizes.
>>
>>
>>> Another question. Considering a model " lme(X ~ A + B + C , random = ~
>>> 1 | Y) " :
>>>
>>> Fixed effects: X ~ A + B + C
>>>                  Value Std.Error DF   t-value p-value
>>> (Intercept)  731.7840 152.55412  7  4.796881  0.0020
>>> A                  0.0100   0.00000  7 -2.467147  0.0430
>>> B             -154.5052  29.89081  7 -5.168986  0.0013
>>> C             -160.3189  34.07642  7 -4.704688  0.0022
>>>
>>> And that I would like to transform it in a prediction equation, how
>>> should I include the mixed effects in the equation: (Intercept*Value +
>>> A*Value + B*Value + C*Value) ? Or when plotting:
>>>> plot( (Intercept*Value + A*Value + B*Value + C*Value), X)
>>>> abline(X~(Intercept*Value + A*Value + B*Value + C*Value)
>>> How should I include the mixed effects on such equation?
>>>
>>> Also, is it possible to add a command that return such equation/plot?
>>> That would be very useful for environmental scientists.
>>>
>>> Again, congratulations/thank you so much for the great package!
>>>
>>> Thank you so much,
>>> Daniel
>>>
>>>
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>>
>>
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>>
>>
>
>
>
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-- 
Friso Muijsers

Institute for Chemistry and Biology of the Marine Environment (ICBM)
Carl-von-Ossietzky University Oldenburg
Schleusenstrasse 1
26382 Wilhemshaven


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