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I want to produce imputations for the missing values using a naive imputation method "Regression imputation " . The first step involves building a model from the observed data then predictions for the incomplete cases are calculated under the fitted model, and serve as replacements for the missing data .

Suppose that we model Ozone by the linear regression function of Solar.R

> library(mice)
> fit <- lm(Ozone ~ Solar.R, data = airquality)
> pred <- predict(fit, newdata = ic(airquality))
# Or alternatively using mice package 
> imp <- mice(airquality[,1:2], method="norm.predict", m=1, maxit=3,seed=1)
> head(airquality[5,1:2])
> head(complete(imp)[5,])

I did not get how the fifth observation is imputed under the fitted model ? , since both Ozone and Solar.R are missing !.

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Your linear regression can't predict on the missing data if it doesn't have a predictor. So your value is not imputed.

Although it does involve regressions, Multivariate Imputation by Chained Equations (MICE) is a bit different from your linear regression approach. In a nutshell, missing variables are first tentatively filled, which makes them suitable as predictors, and then they are iteratively imputed. I would suggest looking at the pseudocode in Azur, M. J.; Stuart, E. A.; Frangakis, C. & Leaf, P. J. (2011) Multiple Imputation by Chained Equations: What is it and how does it work?. International journal of methods in psychiatric research, 20, 40-49 to understand what the algorithm does.

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  • $\begingroup$ Yes, that"s true , but I didn't get how R imputed the observations in which both Ozon and Solar.R are missing !, since the model includes just one predictor Solar.R. Did u check the code (the 5th obs.) ? or this one sum(as.numeric(!is.na(complete(imp)))) ; dim(airquality[,c(1,2)]) $\endgroup$
    – Bahgat Nassour
    Commented Nov 14, 2016 at 17:14
  • $\begingroup$ It is not clear though with only two variables both missing how mice can get started here. $\endgroup$
    – mdewey
    Commented Nov 14, 2016 at 18:32
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    $\begingroup$ Did you read the reference I cited? It explains the algorithm step for step. It works, as I said above, because the NAs are first filled with column means and then iteratively corrected. $\endgroup$
    – Florian Hartig
    Commented Nov 15, 2016 at 7:08

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