[R-sig-ME] Residuals look "mirrored" when using lmer with imputed data

[João C P Santiago]

I'm sorry I may be misunderstanding, but why do you say my data is  
bounded between 0 and 1? The data I shared is from multiple  
measurements of a blood hormone, which can be 0, but is certainly not  
bounded at 1. Next I calculated the AUC i.e. the "total exposure" of  
that hormone. X is time and y is the value of the hormone at each time  
point. The AUC is also not bounded at 0 and 1.

Am I missing something?

Thanks for your reply!
Santiago


Quoting "Alday, Phillip" <Phillip.Alday at mpi.nl>:

> You're fitting a normal/Gaussian LMM to data bounded on [0, 1]. The
> model assumptions about the residuals simply won't hold for bounded,
> binomial-like values. 
>
> Why not fit a binomial model to the data and then use fitted(model) to
> compute AUC of the entire model? 
>
> Phillip  
>
> On Fri, 2017-08-04 at 08:52 +0200, João C P Santiago wrote:
>> I'm trying to assess if a treatment had any effect on the levels of
>> a  
>> hormone. To do this I need to calculate the area under the curve
>> and  
>> then adjust it for sex (a known confounder) and smoking status (not  
>> included in the demo data below to keep things simpler).
>>
>> Here's a dput of the data: https://pastebin.com/VYcQGkwb
>>
>> There's some missing values, so first step is to impute them using
>> the  
>> mice package, then calculate AUC and finally fit the model:
>>
>> library(dplyr)
>> library(lme4)
>> library(mice)
>> library(zoo)
>>
>> ## Impute missing values
>> dfMids <- mice(df, m = 10, maxit = 15, seed = 2535)
>> dfImp  <- complete(dfMids)
>>
>> ## Calculate AUC
>> dfImpAUC <- dfImp %>%
>>    arrange(sampleNum) %>%
>>    group_by(ID, treatment) %>%
>>    mutate(AUC = sum(diff(sampleNum)*rollmean(value,2)))
>>
>> ## Fit model
>> fit <- lmer(AUC ~ sex * treatment + (1|ID), data = dfImpAUC)
>>
>> ## Plot residuals
>> plot(fit)  # output: https://imgur.com/a/vfL1R
>> qqnorm(resid(fit))
>>
>>
>>
>> I know it's possible to fit a model to each iteration of mids
>> model,  
>> but then I can't calculate the AUC, which is what I actually need.
>> Any  
>> ideas why the residuals look like that?
>>
>> Best
>> Santiago
>>
>>
>>
>>



-- 
João C. P. Santiago
Institute for Medical Psychology & Behavioral Neurobiology
Center of Integrative Neuroscience
University of Tuebingen
Otfried-Mueller-Str. 25
72076 Tuebingen, Germany

Phone: +49 7071 29 88981
Fax: +49 7071 29 25016