[R-sig-ME] Random slope/intercept without correlation in lmer

[Jake Westfall]

In my opinion it is best as a general rule of thumb to always manually code your factors into numeric objects before passing them to a model fitting function, whether it is lmer() or lm() or whatever. The R Gods in their wisdom gave us factor objects in an attempt to make life easier for us, but in my experience factors often just get in the way or lead to unexpected results. The present issues are just another example.

Jake

> From: gustaf.granath at gmail.com
> Date: Tue, 12 Aug 2014 10:40:31 -0400
> To: steve.walker at utoronto.ca; bbolker at gmail.com
> CC: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Random slope/intercept without correlation in lmer
> 
> Thanks. Works perfectly.
> 
> For more than 2 levels I guess nlme is still the way to go if you want 
> to manipulate the covariances structure.
> 
> Gustaf
> 
> > The `dummy` function in lme4 might be useful.  Here's the example from 
> > the ?dummy help file:
> >
> > data(Orthodont,package="nlme")
> >      lmer(distance ~ age + (age|Subject) +
> >           (0+dummy(Sex, "Female")|Subject), data = Orthodont)
> >
> > Here's another example:
> >
> > lmer(distance ~ dummy(Sex, "Female") +
> >      (dummy(Sex, "Female") || Subject),
> >      data = Orthodont)
> >
> > Cheers,
> > Steve
> >
> >
> >
> > On 2014-08-11, 6:42 PM, Ben Bolker wrote:
> >> All you need to do is set up your own dummy variable (e.g.
> >> ntreat=as.numeric(treat)-1, or ntreat=as.numeric(treat=="1"), or
> >> ntreat=(original treat variable before using factor(treat) and then use
> >> (ntreat||group) or (1|group)+(0+ntreat|group)
> >>
> >>   This is related but not identical to the last example in ?lmer ; it's
> >> caused by an interaction between the way that R constructs model 
> >> matrices
> >> from factors and the way lme4 uses that functionality.
> >>
> >>
> >>
> >> On Mon, Aug 11, 2014 at 2:38 PM, Gustaf Granath 
> >> <gustaf.granath at gmail.com>
> >> wrote:
> >>
> >>> Hi
> >>> I want to model random slope and intercept without a correlation 
> >>> between
> >>> the two. Is it possible to do this in lmer when the predictor is a 
> >>> factor?
> >>>
> >>> For example, imagine that x has 2 levels (control and treatment). In 
> >>> nlme,
> >>> I have been modeling uncorrelated intercept and slope like this:
> >>> lme(y ~ x, random=list(x = pdDiag(~ group)) ) where group is a random
> >>> factor.
> >>> It gives me the random intercept and random slope (i.e. variation in
> >>> treatment effect among groups).
> >>>
> >>> In lmer, I think the corresponding model is defined as:
> >>> lmer( y ~ x + (x||rand) and I guess this gives me differences 
> >>> (variation
> >>> in differences to the intercept), but it includes a covariance term.
> >>>
> >>> Is it possible to reproduce the above lme() model in lmer?
> >>>
> >>> I have a strong feeling that Im missing something here. Most of the
> >>> literature on this subject (and R-list questions) deals with continuous
> >>> variables so pls let me know if there is a good source on this topic.
> >>>
> >>> Below follows a small example.
> >>>
> >>> Cheers
> >>>
> >>> Gustaf
> >>>
> >>>
> >>> set.seed(1)
> >>> treat = rep(c(0, 1), each = 5, 10)
> >>> group = rep(1:10, each = 10)
> >>> rand.int = rep( rnorm( 10, 0, 1), each = 10)
> >>> rand.slop = rep( rnorm(10, 0, 1), each = 10)
> >>> e = rnorm(100, 0, 0.5)
> >>> y = 10 + rand.int + treat + rand.slop*treat + e
> >>> treat = factor(treat)
> >>>
> >>> #lmer
> >>> library(lme4)
> >>> # with correlation between intercept and slope
> >>> mod = lmer(y ~ treat + (treat|group) )
> >>> # without correlation between intercept and slope
> >>> # gives lots of error msgs
> >>> mod2 = lmer(y ~ treat + (treat||group) )
> >>> summary(mod)
> >>> summary(mod2)
> >>> # var-covar matrix
> >>> VarCorr(mod)$group
> >>> VarCorr(mod2)$group.1 #still a covariance term
> >>>
> >>> #nlme
> >>> # without correlation
> >>> library(nlme)
> >>> lme.mod <- lme(y ~ treat, random=list(group = pdDiag(~ treat)) )
> >>> summary(lme.mod)
> >>> getVarCov(lme.mod)
> >>>
> >>> -- 
> >>> Gustaf Granath (PhD)
> >>> Post doc
> >>> McMaster University
> >>> School of Geography & Earth Sciences
> >>
> >
> 
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