lme4 and PIRLS
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 ------------------------------------------------------------------- King's College London Department of Primary Care and Public Health Sciences Division of Health and Social Care Research 7th Floor Capital House 42 Weston Street London SE1 3QD Tel: 020 7848 6638 Fax: 020 7848 6620 -----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 _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models -------------- next part -------------- A non-text attachment was scrubbed... Name: histBig.png Type: image/png Size: 38605 bytes Desc: histBig.png URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20130725/84048e69/attachment-0001.png>