[R-sig-ME] na.action = na.augment for random effects in lme4?

[Ben Bolker]

On 10/11/20 6:05 PM, Phillip Alday wrote:
> Doesn't look like it, the documentation for predict.merMod has option:
> 
> 
>> allow.new.levels: logical if new levels (or NA values) in ‘newdata’ are
>>            allowed. If FALSE (default), such new values in ‘newdata’
>>            will trigger an error; if TRUE, then the prediction will use
>>            the unconditional (population-level) values for data with
>>            previously unobserved levels (or NAs).
> 
> Maybe packages adding some extra functionality like merTools have some
> things for this.
> 
> 
> Otherwise, you can just filter your newdata with something like
> 
> newdata[newdata$groupingvar %in% levels(olddata$groupingvar), ]
> 
> Phillip
> 

   Yes. Following up:

* do you mean na.exclude (rather than na.augment)?

* it would certainly make sense that you might want these cases to be NA 
rather than predicted at the population level.  In hindsight it might 
have been a good idea to set this up as new.re.levels allowing the 
options c("population","fail", "exclude", "omit").

   Honestly, sorting out and implementing appropriate behaviours for a 
possible combinations of NAs in covariates or grouping variables of the 
initial data set and in the prediction data set has always given me a 
headache ...

    I would say you should do

  newresp <- predict(fitted_model, newdata, allow.new.levels=TRUE)
  new_levels <- !newdata$groupingvar %in% levels(orig_data$groupingvar)
  newresp[new_levels] <- NA

> 
> On 11/10/2020 23:42, Andrew Robinson wrote:
>> Hi all,
>>
>> I'm interested in fitting and applying models for which the data to which I apply the model will have some observations with random effects levels that are not in the fitting dataset.  I would like to flag these observations in some way.
>>
>> Naively, I would prefer to have something like the na.action = na.augment argument so that predictions for observations with previously unseen levels of random effects would simply be missing.  Is there such a capability that I've missed?
>>
>> Warm wishes,
>>
>> Andrew
>>
>>
>> --
>> Andrew Robinson
>> Director, CEBRA and Professor of Biosecurity,
>> School/s of BioSciences and Mathematics & Statistics
>> University of Melbourne, VIC 3010 Australia
>> Tel: (+61) 0403 138 955
>> Email: apro using unimelb.edu.au
>> Website: https://researchers.ms.unimelb.edu.au/~apro@unimelb/
>>
>> I acknowledge the Traditional Owners of the land I inhabit, and pay my respects to their Elders.
>>
>> 	[[alternative HTML version deleted]]
>>
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