[R-sig-ME] lme4 and PIRLS

[Ben Bolker]

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

> 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?

  We're still failing to communicate ... in my world, PIRLS is
an algorithm, not a 'penalisation method'.  The meaning of
'penalisation' in the term is that the conditional deviance
(the deviance of the data conditional on a particular set of
conditional mode estimates) is penalised by the variation of
the conditional modes around zero. 
 
> 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)

  lmer is already using penali[sz]ed least squares (where 'penalised'
is used in the same sense as above), but uses a more specialized
algorithm that works to calculate the profile likelihood of the
theta (RE variance-covariance) parameters.  PIRLS would just be a less
efficient but more general way to arrive at the same answers.
 
> 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.

   There is a 'robustlmm' package on CRAN ... or you could use
the 'ordinal' package to treat your data as ordinal rather than
approximately (conditionally) Normal ...