I carried out multiple imputation using MICE with m=10. The R code is shown below:
RainfallData <- mice(rainfall,m=10,maxit=10,meth='pmm')
modelFit1 <- with(RainfallData,lm(Total.Rainfall~Wind.Direction+Hor.Windspeed+Solar.Radiation+Baro.Pressure+Vpr.Pressure+Rel.Humidity+Air.Temp))
completedData <- complete(RainfallData,action = "long")
My question is how shall I select the best complete dataset out of 10 datasets (m=10) that provides the best estimated values for missing values? I need to use this dataset for further analysis.
Should I take the averages of the values from 10 completed dataset and build one complete dataset? Or shall I just randomly select any out of 10?
In my case, only 2.8% of the data are missing for each variable. I can consider Complete Case Analysis but I would like to study time series model and would like to fill the missing values. Both dependent and independent variables have missing data. The missing data is MCAR.
Please help me. I am really confused.