[R-sig-ME] Cross validated log likelihood, redux.

[Ben Bolker]

  Hmmm. The proximal problem is that the updated deviance function
thinks it needs coefficients for all of the parameters (theta = 3 +
beta=12), not just the theta parameters.

  It's not obvious to me (I know this might be code I wrote in the first
place!) why you're trying to pass the full theta+beta coefficient vector
to a model defined with nAGQ=0 (which estimates the beta coefficients as
part of the penalized weighted resid sum of squares (pwrss)
computation).  Can you explain/remind us of the reason for the mismatch
here?

  cheers
    Ben Bolker


On 2019-08-16 7:55 p.m., Rolf Turner wrote:
> 
> My apologies for continuing to pester the list with questions about this
> issue, but I urgently need an answer to my most recent question.
> 
> That question was contained in a postscript to a reply to D. Rizopoulos,
> and thereby may have been overlooked.  Consequently I am re-posting this
> question.  (Again I apologise for taking up bandwidth.)
> 
> In summary, the situation is as follows:
> 
> * I am trying to calculate the log likelihood of a "new" data set on the
> basis of a model fitted to a different data set.
> 
> * Ben Bolker showed me how to do this using glmer() (from the lme4
> package) and after some to-ing and fro-ing I got his recipe to work.
> 
> * Dimitris Rizopoulis also showed me how to do this using the
> mixed_model() function from the GLMMadaptive package.  Again, after much
> delay and after more to-ing and fro-ing, I got Prof. Rizopoulis's advice
> to work.
> 
> * I then wanted to cross-check the value obtained from mixed_model()
> with that obtained from glmer(), so I re-ran the glmer() based code.
> Lo and behold, that code threw an error (where it had not before done so).
> 
> I would really like to be able to use both methods (i.e. that based on
> mixed_model() and that based on glmer()).  So I would like to figure out
> what is going wrong --- or what I am doing wrong --- in the case of the
> glmer() approach.
> 
> I have attached a source-able script demo.glmer.txt to demonstrate what
> happens, and have also attached the (simulated) data set X.txt which the
> script uses.
> 
> If you place demo.glmer.txt and X.txt in your working directory and
> source("demo.glmer.txt") you will get the following message and error
> message:
> 
>> fixed-effect model matrix is rank deficient so dropping 1 column /
>> coefficient
>> (15!=3)
>> Error in pp$setTheta(theta) : theta size mismatch
> 
> As I said in my (possibly overlooked) postscript in a previous posting
> on this issue:
> 
> This code *worked* previously!!!
> 
> I thought that the dropped coefficient might be the problem, so in the
> demo script I replaced coefs by coefs[-15] but then the error simply
> changes to:
> 
>> (14!=3)
>> Error in pp$setTheta(theta) : theta size mismatch
> 
> Indeed 15!=3 (no shit, Sherlock!) and likewise 14!=3.  But why *3*???
> 
> If I do
> 
> chk <- update(g.trn,data=VS)
> coefs.chk <- unlist(getME(chk,c("theta","beta")))
> 
> I get coefs.chk to be a vector of length 14 (which seems to line up,
> names-wise, with coefs as produced by the script) so 14 seems to be the
> "right answer".  Where does 3 come in?
> 
> Initially (way back when) I got an error *something* like this, but then
> Ben Bolker advised me to add the argument
> 
>     "control=glmerControl(check.nobs.vs.nRE="ignore")"
> 
> to the call to update() and that fixed the problem that I was having.
> But the fix no longer seems to be "operative". :-)
> 
> What has changed?  Is there any way I can get this to work?
> 
> Thanks for any pearls of wisdom.
> 
> cheers,
> 
> Rolf Turner
> 
> 
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