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
> 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
Solar.R are missing !.