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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))

pool(modelFit1)

summary(pool(modelFit1))

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.

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You should fit your model to each of the multiple imputations and then combine the results (e.g. using Rubin's rule). That way the uncertainty about your final analysis result does not just come from the sampling variability of the chosen probability distribution, but also from how much the results from the different imputed datasets differ. That appropriately reflects the uncertainty about what the missing data might have been.

If you average the results from fewer than 3-5 imputation (e.g. by using just one imputation), you get none of the nice properties of MI. E.g. your standard errors will be too small and you get type I error inflation. If you pick 1 imputation based on done model fit statistics, I would expect this to get even worse.

10 imputations is a relatively low number and if it does not take too long, I would normally do at least 250 or so. Doing so often makes your standard errors a little smaller and makes the results less dependent on your random number seed.

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  • $\begingroup$ Thank you. I am still confused when you said combine the results. I know this can be done using pool() with MICE package in R, but I need one complete dataset which I can use it to build time series model, ANN models etc. Please help. $\endgroup$ – Ravinesh Chand Jul 17 '19 at 4:27
  • $\begingroup$ Build these models on each imputed dataset, then combine your inference. $\endgroup$ – Björn Jul 17 '19 at 4:28
  • $\begingroup$ I now understand but if you even have 50 datasets, building model on each will be challenging. Is there any other way of selecting the best one? Please help. $\endgroup$ – Ravinesh Chand Jul 17 '19 at 4:42
  • $\begingroup$ You could impute and then summarize the imputed values (e.g. as mean and SD on a suitable scale), if your subsequent models can deal with that as an input. $\endgroup$ – Björn Jul 17 '19 at 5:41

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