lmer vs lmer2

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.

Andy

-----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

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.