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

Alday, Phillip Phillip.Alday at mpi.nl
Sat Aug 5 08:41:11 CEST 2017

```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
>
>
>
>
```