When I run regression analysis I find it important to run some model diagnostics, such as detection of outliers, influential observations, multi-collinearity (much like these examples http://www.statmethods.net/stats/rdiagnostics.html).
Example of Diagnostics I use:
#Assessing the Assumption of Independence, using Durbin Watson Test dwt(lmModel) #Controlling for Multicollinearity vif(lmModel) 1/vif(lmModel) mean(vif(lmModel))
I have a sample with a lot of missing data across most variables. Thus, I need to use multiple imputations.
However, model diagnostics seems to be impossible to explore when using multiple imputations. So far, I have used the mice package and since I am still a novice at R my multiple imputation script basically looks like this:
#Imputes 5 datasets imp <- mice(myData, m=5) #Runs regression analysis on each imputed dataset fit <- with(imp, lm(A ~ B + C)) #Pools the results pooled <- pool(fit) summary(pooled)
Is there some way to use the diagnostic test on the pooled data? or do I have to use diagnostic tests on each imputed dataset (before being pooled)? or is there some other smart way of solving this issue?
Thanks for your time