[R-sig-ME] lme4 and PIRLS

[Ben Bolker]

Grace, Justin <justin.grace at ...> writes:

> 
> Dear group,

> I have been advised that I need to use penalised iteratively
> reweighted least squares (PIRLS) to improve some of my lmer models,
> rather than my current REML approach.
 
> I have spent a fair bit of time using mixed models but this is new
> to me, I was wondering if someone could explain whether this can be
> implemented in or on top of lme4, if there is a package to do so, or
> if I need to code manually.  Also, why and how this is an
> improvement.
 
> The purpose of our models is to build patient-specific growth curves
> and then use these models to predict a new patient's growth and then
> improve this model after some observations have been made.

  PIRLS is the algorithm that glmer uses; it allows the variance of
the residuals to be a specified function of the mean rather than being
constant as in the standard linear mixed model.  Typically, you would
use PIRLS (automatically) when you decided to use a generalized mixed
model because your data represented (e.g.) counts or proportions.

  I don't feel I have quite enough context to answer your other
questions. If someone has advised you that you should use PIRLS, can
you go back and ask *them* why it's an improvement?

  Just to clarify, "REML" and "ML" are _criteria_ for fitting,
wherease "PIRLS" is an _algorithm_ (it is generally used to fit
a ML criterion).

  Ben Bolker