same model runs in nlme but not lme4

   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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