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

[Ben Bolker]

    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 
> <mailto: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
>>     <mailto: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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