same model runs in nlme but not lme4

   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 at 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 at 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 ###

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