[R-sig-ME] Group-level predictors which impact the random intercept

[Yashree Mehta]

Thank you very much for your response and explanation.

Regards,
Yashree

On Mon, Jun 11, 2018 at 6:11 PM, Douglas Bates <bates using stat.wisc.edu> wrote:

> Thank you for transferring the discussion over to the R-SIG-Mixed-Models
> group.
>
> As I mentioned in the email discussion, the issue of covariates in the
> fixed-effects terms and whether or not they vary within the levels of a
> grouping factor for random-effects terms is a consequence of the way the
> model is described in the multilevel modeling literature.  In other words,
> there is no inherent problem with defining a mixed-effects model involving
> a fixed-effect for Age even though Age does not change within
> Household_ID.  When multilevel models were being formulated many years ago
> an approach to how one would estimate the parameters leaked over into the
> model definition.  It became important to formulate models within models
> within ... but that approach is unnecessary and led to many
> misconceptions.  Furthermore, the approach is too restrictive.   A
> multilevel model cannot accommodate crossed random effects, such as subject
> and item, or partially crossed random effects such as child, teacher and
> school in longitudinal data.
>
> To me one of the most important innovations in the lme4 package was to
> reformulate the evaluation of the deviance for a linear mixed-effects model
> as a penalized least squares problem and to employ a sparse Cholesky
> factorization to solve a modified version of Henderson's mixed-model
> equations.  This is described in our 2015 J. of Statistical Software
> paper.  It is not important for every user of the lme4 package to
> understand the mathematics of the derivation but it helps to know that the
> model can be formulated and the parameters can be estimated as described
> there.  The fact that other and, I think it is fair to say, inferior
> formulations and estimation methods exist is not relevant.
>
> On Sun, Jun 10, 2018 at 12:00 PM Yashree Mehta <yashree19 using gmail.com>
> wrote:
>
>> Hello,
>>
>> I had recently posted the following for understanding the syntax for
>> adding
>> group-level predictors in a random intercept model:
>>
>> """""
>>
>> Hi,
>> I am working with a random intercept model. I have the usual "X" vector of
>> covariates and one id variable which will make up the random intercept.
>> Now
>> I wish to add group-level predictors (which are NOT in the X vector) such
>> that the random intercept depends on these predictors.
>> For example,
>> Response variable: Production of maize
>> Covariate: Size of plot
>> Group-level predictor: Age of farmer
>> ID variable: Household_ID
>>
>> I wish to confirm the syntax for including the group-level "Age of farmer"
>> variable.
>> fit<-lmer(Production~ Size+ Age+ (1|Household_ID), data=data)
>>
>> Is this correct or is there another way of declaring the group-level
>> predictor in the formula?
>>
>> """""
>>
>> This syntax had been confirmed as correct. Now I am wondering how does
>> lmer
>> really distinguish between the usual X covariates and group-level
>> predictors? We have not really differentiated them in the formula. How
>> does
>> lmer construe Age to only impact the random intercept?
>>
>> Thank you very much,
>>
>> Regards,
>>
>> Yashree
>>
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>>
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>

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