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

[Steve Walker]

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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
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