same model runs in nlme but not lme4

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]]

   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]]

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 
<mailto: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 ###

    >
    >? ? ? ?[[alternative HTML version deleted]]
    >
    > _______________________________________________
    > R-sig-mixed-models at r-project.org

    <mailto:R-sig-mixed-models at r-project.org> mailing list

    > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

   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 ###

      [[alternative HTML version deleted]]

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 
<mailto: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
    <mailto: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 ###

        >
        >? ? ? ?[[alternative HTML version deleted]]
        >
        > _______________________________________________
        > R-sig-mixed-models at r-project.org

        <mailto:R-sig-mixed-models at r-project.org> mailing list

        > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

   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 ###

      [[alternative HTML version deleted]]

Many thanks!

On Fri, May 22, 2020 at 6:35 PM Ben Bolker <bbolker at 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 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 ###

      [[alternative HTML version deleted]]