[R-sig-ME] Specifying a model with a link function in MCMCglmm

[Greg Snow]

You can follow my example and create the shortcut and  then have R
open minimized (you set that in the windows shortcut properties).

I don't know of a way to not have any R window open.

On Thu, Feb 18, 2016 at 10:57 AM, Sam H <nirvana4lf at gmail.com> wrote:
> Thanks for your response Jarrod! Regarding the prior specification, I
> forgot to mention the reason I did that. Originally I specified the model
> with no prior, but it kept failing just before the 1000th iteration with
> the error message "ill-conditioned G/R structure". I was not very sure what
> to do since I don't really have any estimates to use for a prior, so I
> figured I would just use estimates produced by lmer. Should I just set the
> G structure to an identity matrix, or does it not really matter since nu is
> high?
>
> On Thu, Feb 18, 2016 at 12:48 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
> wrote:
>
>> Hi Sam,
>>
>> Each distribution has a fixed link function in MCMCglmm, and an inverse
>> link function for a Gaussian response would be hard to implement.
>>
>> Also, using a REML estimate as a prior, and then having quite a strong
>> degree of belief parameter is a bit odd. Many, including myself, would
>> consider this double-dipping.
>>
>> Cheers,
>>
>> Jarrod
>>
>>
>>
>> On 17/02/2016 22:26, Sam H wrote:
>>
>>> Hello all,
>>>
>>>
>>> Before jumping into my question, let me first briefly explain my model to
>>> give context. Here is how I currently have specified the model using
>>> MCMCglmm, first specifying a model in lmer and extracting the variance
>>> estimates for my G prior:
>>>
>>> full_lmer <- lmer(RT ~ AgeGroup*cue*cong + (cue + cong|PID), data =
>>> vsdataset)
>>>
>>> sigma2 <- sigma(full_lmer)^2
>>>
>>> Lambda <- getME(full_lmer, "Lambda")
>>>
>>> Sigma <- sigma2*tcrossprod(Lambda)
>>>
>>> Gmer <- Sigma[1:4,1:4] #Extracting the VCV parameters from the block
>>> diagonalized Sigma
>>>
>>> full_mcmc <- MCMCglmm(RT ~ AgeGroup*cue*cong, random = ~us(1 + cue +
>>> cong):PID, thin = 10, nitt = 20000, data = vsdataset, prior = list(G =
>>> list(G1 = list(V = diag(diag(Gmer)), nu = 5))))
>>>
>>> Note: I used V = diag(diag(Gmer)) due to the fact that Sigma/Gmer was not
>>> positive definite, which MCMCglmm would not accept.
>>>
>>>
>>> Quick explanation of model terms:
>>>
>>> RT --> response time in msec, very positively skewed even after outlier
>>> removal, inverse transform seems to center it
>>>
>>> AgeGroup --> 2 level factorial var (Old and Young, between-subjects)
>>>
>>> cue --> 3 level factorial var (within-subjects)
>>>
>>> cong --> 2 level factorial var (within-subjects)
>>>
>>> PID --> participant ID number
>>>
>>>
>>> I want to specify this model with an inverse/reciprocal link function
>>> (Gaussian family). However, I can't figure out how to specify the link
>>> function. In the help section for the MCMCglmm function, they mention a
>>> "linking.function" for the random effects terms, but it doesn't seem to
>>> have anything to with specifying a link function for the response
>>> variable.
>>> According to the course notes from the MCMCglmm package, "there are many
>>> different types of distribution and link functions and those supported by
>>> MCMCglmm can be found in Table 7.1." However, Table 7.1 seems to just list
>>> the families and their PDFs, there's no column listing "supported" link
>>> functions.
>>>
>>> So, how do you specify a link function using MCMCglmm? If you can't
>>> directly specify a link function, is there something else I need to do
>>> such
>>> as specifying the prior a certain way, or is it valid to just specify the
>>> model as having a gaussian response and leave the mode as I've specified
>>> it? After plotting the model, I noticed that several of the parameter
>>> distributions were extremely skewed (some left, some right).
>>>
>>>
>>> As a side note, I originally tried two alternatives:
>>>
>>> 1) using lmer with an inverse transform
>>>
>>> 2) using glmer with family = gaussian(link = "inverse") and family =
>>> inverse.gaussian(link = "identity")
>>>
>>>
>>> #1 seems problematic due to the fact that I need to convert the response
>>> variable units back to the original units, which not only flips any
>>> confidence intervals but also makes them uneven. I'm not sure if
>>> converting
>>> these CI's is even appropriate as they were computed with different
>>> units/distribution. I also don't know of any way to validly convert the
>>> standard error back since that is certainly not valid once I
>>> back-transform.
>>>
>>> #2 gave me some issues: first, I had to scale down RT by a factor of 1000
>>> (from ms to s) when using gaussian(link = "inverse") otherwise I would get
>>> an error about the downdated VtV not being positive definite. But after
>>> dividing RT by 1000, it was able to continue, but the model did not fully
>>> converge (I think the max abs gradient was approximately .02). I decided
>>> to
>>> rerun the model after changing the contrasts on my variables from the
>>> default dummy coding to effect coding (using contr.sum). The same thing
>>> happened, except this time the max gradient was a little higher (about
>>> .0375) and in addition, I got the "model is nearly unidentifiable" warning
>>> due to a large eigenvalue. When I ran the model with inverse.gaussian(link
>>> = "identity"), it worked without scaling down RT by 1000 but I a bunch of
>>> optimizer warnings so I scaled it down and this time it wasn't able to
>>> converge because the max abs gradient value was about .0247.
>>>
>>>
>>> Any help on this would be greatly appreciated!
>>>
>>>
>>> - Sam
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> 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.
>>
>>
>
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>
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-- 
Gregory (Greg) L. Snow Ph.D.
538280 at gmail.com