[R-sig-ME] lmer vs lmer2

Martin Maechler maechler at stat.math.ethz.ch
Wed Sep 12 15:46:46 CEST 2007

>>>>> "DB" == Douglas Bates <bates at stat.wisc.edu>
>>>>>     on Thu, 6 Sep 2007 11:17:17 -0500 writes:

    DB> On 9/6/07, Bush, Andrew J <abush at utmem.edu> wrote:
    >> Dear Douglas,

    >> In frustration, I invoked lmer2 this morning and I'm pleased to be able
    >> to tell you that lmer2 copes well and quickly with the model having a
    >> random intercept and two random covariate slopes.  I have not been able
    >> to get lmer to converge for the model on the same data.

    DB> Thanks for the information.

    DB> I expect to remove the confusion between lmer and lmer2 in the near
    DB> future.  The development version of the lme4 package has an lmer
    DB> function that is close to the current lmer2 in design.  It should
    DB> exhibit the same convergence behavior and be slightly faster on models
    DB> fit to large data sets than is the current lmer2.

    DB> This version has been in development for longer than I had expected.
    DB> I still have a few "infelicities" to resolve in the Laplace method for
    DB> generalized linear mixed models before I make test versions available.

    DB> I would be interested in the data set if you would be willing to
    DB> provide it.  I could perhaps incorporate it in the lme4 package so
    DB> others would have access to it.

Yes, indeed.  
The example might be particularly interesting as test case, not
only because some software implementations "converge" with
singular covariance matrix, but also because it
differs from other examples in having "many" fixed effects and
just one level random effects.


    >> -----Original Message-----
    >> From: dmbates at gmail.com [mailto:dmbates at gmail.com] On Behalf Of Douglas
    >> Bates
    >> Sent: Wednesday, September 05, 2007 9:22 PM
    >> To: ajbush at bellsouth.net
    >> Cc: r-sig-mixed-models at r-project.org
    >> Subject: Re: [R-sig-ME] Specifying random effects for multiple
    >> covariates via lmer
    >> On 9/5/07, Andy Bush <ajbush at bellsouth.net> wrote:
    >> > While working through the text "Applied Longitudinal Analysis" by
    >> > Fitzmaurice, Laird and Ware, I encountered a fairly simple case study
    >> (pp
    >> > 210-7) in which a longitudinal model specifies three random effects:
    >> (1)
    >> > random intercepts for id, (2) random slopes for covariate1 (Age | id),
    >> and
    >> > (3) random slopes for covariate2 (log(ht) | id).  I've had no
    >> difficulty
    >> > formulating lmer models with correlated random intercepts and slopes
    >> for
    >> > either of the covariates individually but have not succeeded when I
    >> try to
    >> > compose a model with correlated random intercepts and slopes for two
    >> > covariates.
    >> > Following is code that works well with the individual covariates
    >> separately:
    >> > m1=lmer(LFEV1~Age + loght + InitAge + logbht + (1 + Age |
    >> id),data=fev,
    >> >        na.action=na.omit, method="REML")
    >> > m2=lmer(LFEV1~Age + loght + InitAge + logbht+(1 + loght |
    >> id),data=fev,
    >> >        na.action=na.omit, method="REML")
    >> Maybe I am missing the point but wouldn't the model you are
    >> considering be written as
    >> lmer(LFEV1 ~ Age + loght + InitAge + logbht + (loght + Age|id), data =
    >> fev, na.action = na.omit, method = "REML")
    >> That provides correlated random effects for the intercept, the
    >> coefficient for loght and the coefficient for Age at each level of the
    >> id factor.

    DB> _______________________________________________
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