I am doing data imputation with the missForest function (in the missForest package in R) and everything seems to work just fine. The function is easy to use and, at first sight, imputed data look plausible.
However, I am confuse at how to interpret the different out-of-bag errors (OOBerror).
When I use the default function, I get an OOBerror of 0.1574 (Normalized Root Mean Squares Error or NRMSE):
> mf_test4 = missForest(test.mf4) > mf_test4$OOBerror NRMSE 0.1574
This suggests that, for the whole dataset, data are imputed with 15,74 % error.
When I want to look at the error associate with each imputed variables, I get much smaller errors (the 2 first variables contain no NA, so MSE = 0 since no imputation is needed):
> mf_test42 = missForest(test.mf4, variablewise = TRUE) > mf_test42$OOBerror MSE MSE MSE MSE MSE 0.00000000 0.00000000 0.00040559 0.00005102 0.00027467
I understand that in the second case, we get the MSE (Mean Squared Error... I guess). But even when calculating the squared root of these MSE, I obtain much smaller values than the 0.1574 for the whole imputed dataset:
> sqrt(mf_test42$OOBerror) MSE MSE MSE MSE MSE 0.000000 0.000000 0.020139 0.007143 0.016573
Does that mean that data are imputed for variable 3, 4 and 5 with 2.0 %, 0.07 % and 1.6 % error respectively ? Why is it much smaller than the 15,74 % found for the whole dataset ? I guess it has something to do with normalization ?
Anyone can help me interpret or understand the difference ?