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

[Gustaf Granath]

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
>>
>