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  3. Handling Missing Data with Imputations in R
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Exercise

Drawing from conditional distribution

Simply calling predict() on a model will always return the same value for the same values of the predictors. This results in a small variability in imputed data. In order to increase it, so that the imputation replicates the variability from the original data, we can draw from the conditional distribution. What this means is that instead of always predicting 1 whenever the model outputs a probability larger than 0.5, we can draw the prediction from a binomial distribution described by the probability returned by the model.

You will work on the code you have written in the previous exercise. The following line was removed:

  preds <- ifelse(preds >= 0.5, 1, 0)

Your task is to fill its place with drawing from a binomial distribution. That's just one line of code!

Instructions

100 XP
  • Overwrite preds by sampling from a binomial distribution.
  • Pass the length of preds as the first argument.
  • Set size to 1.
  • Set prob to the probabilities returned by the model.