I have simple question after running multiple imputation what the purpose of pooling?
Suppose if i run a multiple imputation using method cart , after running this imputation technique i get very stable density plot between observed and imputation values. So my understanding is that after that i can assign any imputation to the data which are missing as my density plot telling me imputation process is fine for all m=20
densityplot(imp, xlim = c(2.5, 80), ylim = c(0, 0.1))
After that what is the purpose of pooling?
model1 <- with(imp,lm(Age~ X1+X2+X3)) summary(pool(model1)) pool1<-pool(model1)
What is pooling usage in imputation? Do we really get some value from pooling and impute into data.combine_subset$Age which has null value?