[R-sig-ME] MCMCglmm predict function output and interpretation

[Jarrod Hadfield]

Hi Justine,

eta_1 and eta_635 can be negative. If you make them both -10 for  
example, then Pr(A) is close to one.

jarrid


Quoting Justine Smith <jsmith5 at ucsc.edu> on Tue, 29 Jul 2014 10:55:28 -0700:

> Hi Jarrod,
>
> This is great. I have one final question: using the above equations, I am
> finding it impossible for Pr(A) to ever be the greatest (in fact, Pr(C ) is
> always largest using my data set). Even if I put in tiny values for eta_1
> and eta_635 (0.0001 and 0.0001), all values just become essentially equal.
> See results from some made-up combinations below:
>
>
>   eta_1 eta_635 Pr (A)  Pr (B) Pr (C )  0.0001 0.0001 0.333311111
> 0.333344444 0.333344444  0.0001 0.5 0.274061108 0.274088515 0.451850377  0.5
> 0.0001 0.274061108 0.451850377 0.274088515
>
> Why would this be the case? I know from the data that A is in fact the most
> common outcome.
>
> Best,
> Justine
>
>
>
> On Tue, Jul 29, 2014 at 1:38 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
> wrote:
>
>> Hi Justine,
>>
>> If you get the predictions on the link scale, and denote these as eta_1
>> and  and eta_635 for the first observation, then
>>
>> Pr(A) = 1/(1+exp(eta_1)+exp(eta_635))
>> Pr(B) = exp(eta_1)/(1+exp(eta_1)+exp(eta_635))
>> Pr(C) = exp(eta_635)/(1+exp(eta_1)+exp(eta_635))
>>
>> There is some code for doing this (and marginalising any random effects)
>> in the CourseNotes (p97 after Eq. 5.7).
>>
>> Cheers,
>>
>> Jarrod
>>
>>
>>
>> Quoting Justine <jsmith5 at ucsc.edu> on Mon, 28 Jul 2014 23:45:09 +0000
>> (UTC):
>>
>>  Jarrod Hadfield <j.hadfield at ...> writes:
>>>
>>>
>>>> Hi Justine,
>>>>
>>>> The first 634 predictions are for B vs A, and the second 634 are for C
>>>> vs A. If you want the predicted probabilities of falling in category
>>>> A, B or C you'll have to do it by hand I'm afraid.
>>>>
>>>> Cheers,
>>>>
>>>> Jarrod
>>>>
>>>>
>>> Hi Jarrod,
>>>
>>> Thanks so much for clearing that up. Just to make sure I'm absolutely
>>> clear,
>>> if column 1 is 0.228, and column 635 is 0.092, than for data point #1 the
>>> probability option B is more likely than option A is 0.228 and C more
>>> likely
>>> than A is 0.092? Does this indicate that A (the reference value) is the
>>> most
>>> likely? Can I calculate its relative probability by subtracting the other
>>> two values from 1? I'm happy to assign the categories by hand, but I want
>>> to
>>> make sure I am interpreting the output correctly.
>>>
>>> Thanks again,
>>>
>>> Justine
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>>
>>
>>
>> --
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>
>
> --
> Justine A. Smith
> PhD Student
> Department of Environmental Studies
> University of California, Santa Cruz
> 1156 High St.
> Santa Cruz, CA 95064
> people.ucsc.edu/~jsmith5
> santacruzpumas.org
> conservationscats.com
>



-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.