[R-sig-ME] lme4 and PIRLS

[Grace, Justin]

Hi Ben,

Thanks for your response.

I think I rushed my question - I am aware of the distinction between PIRLS as a penalisation method and REML as an assessment of fit. Is there an equivalent penalisation routine run in lmer?

I am using lmer, not glmer (the outcome is pseudo-continuous - a 20 item score, but with some count-like properties over time and a ceiling effect: see graph, BI is the outcome)

When we include individual and temporal random effects the residuals appear normal. There is a lot of noise however, and since the model is to be used as a prognostic tool in new populations I want to make sure the predictions are robust and not over fitting.

I have validated in external data sets in addition to using cross-validation procedures internally.



Thanks,
Justin



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-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ben Bolker
Sent: 25 July 2013 13:56
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] lme4 and PIRLS

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

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