[R-sig-ME] same model runs in nlme but not lme4

[Simon Harmel]

Just curious, *as.data.frame(profile(fitted glmmTMB model))* e.g., as
demonstrated HERE
<https://rdrr.io/cran/glmmTMB/man/profile.glmmTMB.html>, doesn't
return a "zeta" column as in lme4 models, rather it contains a column
called "value", what is "value" and why we take its square root when
plotting the likelihood profile?

(p.s. I'm assuming a V pattern in the likelihood profile plot would confirm
the health of Wald CIs for glmTMB models, right?)

Thanks, Simon

On Fri, May 22, 2020 at 6:42 PM Simon Harmel <sim.harmel using gmail.com> wrote:

> Many thanks!
>
> On Fri, May 22, 2020 at 6:35 PM Ben Bolker <bbolker using gmail.com> wrote:
>
>>    They're pretty separate things.  The  likelihood profile is completely
>> conditional on the model.  I suppose if the data are completely insane then
>> the profile will probably be weird too.  The profile has to do with the
>> shape of the likelihood surface rather than the distribution of the
>> variation around the model.
>> On 5/22/20 7:24 PM, Simon Harmel wrote:
>>
>> Short but very clear. Appreciate it very much. Don't mean to make this
>> long, but how this likelihood profile analysis relates with fitted vs.
>> residual relation? Can they be at odds?
>>
>> On Fri, May 22, 2020 at 5:41 PM Ben Bolker <bbolker using gmail.com> wrote:
>>
>>>
>>>    Profile plots expressed in terms of the signed square root are
>>> straight lines if the log-likelihood surface is quadratic (in which case
>>> the Wald confidence intervals will be reliable). (I know that's very terse
>>> but I'm composing in haste.)
>>>
>>>   vignette("lmer", package="lme4") has a little bit.  More generally you
>>> can read in any advanced stats book about likelihood profiles and what they
>>> are/mean (section 4 of https://ms.mcmaster.ca/~bolker/emdbook/chap6A.pdf
>>> gives one such introduction).
>>> On 5/22/20 6:35 PM, Simon Harmel wrote:
>>>
>>> Many thanks, Ben. Just curious, what information do the plots at the end
>>> of your exactly convey?
>>>
>>> I also appreciate it if there if you could point me to a
>>> documentation in lme4 where I can learn more about `profile()` and its
>>> output.
>>>
>>> Many thanks, Simon
>>>
>>> On Fri, May 22, 2020 at 5:25 PM Ben Bolker <bbolker using gmail.com> wrote:
>>>
>>>>    Because lme4 is fussier than lme.  lme will fit models where the
>>>> variance components are jointly unidentifiable; lmer tries to detect
>>>> these problems and complains about them.  It's possible that this is a
>>>> false positive.  You can make it run by specifying
>>>>
>>>> m1 <- lmer(y~ group*year + (year|stid), data = dat,
>>>> control=lmerControl(check.nobs.vs.nRE="ignore"))
>>>>
>>>>    but I strongly recommend that you think about whether this might be
>>>> exposing problems.
>>>>
>>>>   calculating the profile suggests a little bit of weirdness.
>>>>
>>>> pp <- profile(m1,signames=FALSE)
>>>>
>>>> dd <- as.data.frame(pp)
>>>>
>>>> library(ggplot2)
>>>> ggplot(dd,aes(.focal,.zeta)) + geom_point() + geom_line() +
>>>> facet_wrap(~.par,scale="free_x")
>>>>
>>>> You can compare confint(pp) to intervals(m2); they're mostly
>>>> consistent,
>>>> but some caution is suggested for the CIs on the correlation and the
>>>> year SD
>>>>
>>>>
>>>> On 5/22/20 5:57 PM, Simon Harmel wrote:
>>>> > Hi All,
>>>> >
>>>> > I was wondering why my model runs ok when I use `nlme` package but it
>>>> fails
>>>> > when I use the `lme4` package, am I missing something?
>>>> >
>>>> > Thanks, Simon
>>>> >
>>>> > #===================================
>>>> > library(lme4)
>>>> > library(nlme)
>>>> >
>>>> > dat <- read.csv('
>>>> https://raw.githubusercontent.com/hkil/m/master/z.csv')
>>>> >
>>>> > m1 <- lmer(y~ group*year + (year|stid), data = dat)       ## Fails ###
>>>> >
>>>> > m2 <- lme(y~ group*year, random = ~year|stid, data = dat) ## Runs ###
>>>> >
>>>> >       [[alternative HTML version deleted]]
>>>> >
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>>>>
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>>>

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