[R-sig-ME] Log likelihood of a glmer() binomial model .
Rolf Turner
r@turner @end|ng |rom @uck|@nd@@c@nz
Sun Apr 21 01:03:17 CEST 2019
On 21/04/19 2:51 AM, Ben Bolker wrote:
> I agree with Juho that there's a typo -- would be something like.
>
> sum((y==1)*log(pred_prob) + (y==0)*log(1-pred_prob))
Whoops. Not a typo as such but a failure (lapse) in my understanding.
I was (stupidly) thinking that predict(...,type="response") would
produce Pr(Y = y_i | predictors_i) but *of course* (!!!) it doesn't ---
it produces Pr(success | predictors-i). So I need to invoke dbinom(),
as Prof. Ruohonen has pointed out. I think I "really" knew that, but
somehow my brain slipped a cog.
Sorry for the confusion.
That notwithstanding, (again as Prof. Ruohonen pointed out) this is
still not right since predict() produces Pr(success | predictors_i, R =
0) whereas I need Pr(success | predictors_i) with the random effect R
"integrated out".
> As far as what predict() does: depending on the value of re.form, it
> either gives a population-level prediction (R=0) or a conditional
> prediction (R set to its conditional mode). You might be looking for
> *marginal* predictions (which Rizopoulous's GLMMadaptive package can do ...)
Oh dear. I would then have to switch over from using lme4 to using
GLMMadaptive .... Maybe I'll have to climb that mountain. Or maybe
I'll go with the "slow" method of calculating cross-validated likelihood
that Ben Bolker outlined in a previous email.
cheers,
Rolf
> On 2019-04-20 3:42 a.m., Juho Kristian Ruohonen wrote:
>> Rolf: Forgive my ignorance, but isn't the relevant log-likelihood here
>> the log-likelihood of the observed responses in the validation set given
>> the model-predicted probabilities? I.e. sum(dbinom(VS$y, size = VS$n,
>> prob = predict(fit, newdata = VS, type = "response"), log = TRUE))? Even
>> this would be somewhat off because dbinom() isn't aware of the
>> random-effects integral business. But it looks to me like your current
>> call is calculating the log-sum of the predicted probabilities of y = 1
>> in the validation set, not the loglikelihood of the observed responses
>> in the validation set.
>>
>> la 20. huhtik. 2019 klo 8.51 Rolf Turner (r.turner using auckland.ac.nz
>> <mailto:r.turner using auckland.ac.nz>) kirjoitti:
>>
>>
>> On 20/04/19 12:44 PM, Ben Bolker wrote:
>>
>> > This seems wrong.
>>
>> Yeah, that figures.
>>
>> > The GLMM log-likelihood includes an integral over
>> > the distribution of the random effects.
>>
>> I was aware of this. I guess what I was naïvely expecting was that
>> predict.merMod() would handle this. I.e. that this predict method
>> (with type = "response") would return, for each observed y_i in the
>> (new) data set
>>
>> Pr(Y = y_i) = \int_0 Pr(Y = y_i | R = r) f(r) dr
>>
>> where R is the vector of random effects and f(r) is its probability
>> density function (multivariate normal, with mean 0 and some covariance
>> matrix, which has been estimated in the fitting process.
>>
>> I guess that this is *not* what predict.merMod() returns --- but I
>> don't
>> understand why not. It seems to me that this is what it "should"
>> return.
>>
>> I'm probably misunderstanding something, possibly simple, possibly
>> subtle.
>>
>> Apropos of nothing much, what *does* predict.merMod() return?
>> Maybe Pr(Y = y_i | R = 0) ???
>>
>> <SNIP>
>> > Here is an **inefficient** method for computing the likelihood
>> >
>> > coefs <- unlist(getME(fit,c("theta","beta"))
>> > newdev <- update(fit, data=VS, devFunOnly=TRUE)
>> > newdev(coefs)
>> >
>> > This is slow because it has to reconstruct all of the random-effects
>> > matrices, do permutations to reorder the relevant matrices to be as
>> > sparse as possible, etc. etc.
>>
>> Thanks for this. I'll give it a go. I think that the slowness may not
>> be an overwhelming drawback. Anyhow I shall try to test it out.
>>
>> Thanks again.
>>
>> cheers,
>>
>> Rolf
>>
>> --
>> Honorary Research Fellow
>> Department of Statistics
>> University of Auckland
>> Phone: +64-9-373-7599 ext. 88276
>>
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--
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Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
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