Residuals look "mirrored" when using lmer with imputed data
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, Jo?o 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