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  3. Handling Missing Data with Imputations in R
  • 1

    The Problem of Missing Data

    Free

    In this chapter, you’ll find out why missing data can be a risk when analyzing a dataset. You’ll be introduced to the three missing data mechanisms and learn how to recognize them using statistical tests and visualization tools.

  • 2

    Donor-Based Imputation

    Get to know the taxonomy of imputation methods and learn three donor-based techniques: mean, hot-deck, and k-Nearest-Neighbors imputation. You’ll look under the hood to see how these methods work, before learning how to apply them to a real-world tropical weather dataset. Along the way, you’ll also learn useful tricks that you can use to make them work even better for your problems.

  • 3

    Model-Based Imputation

    It’s time to learn how to use statistical and machine learning models, such as linear regression, logistic regression, and random forests, to impute missing data. In this chapter, you’ll look into how the models make their predictions and use this knowledge to draw the imputed values from conditional distributions. This is important as it ensures your imputations are more varied and plausible, making them more similar to the true data.

  • 4

    Uncertainty from Imputation

    Imputed values are not set in stone. They are just estimates and estimates come with some uncertainty. In this final chapter, you’ll discover how bootstrapping and chained equation using the mice package can be used to incorporate imputation uncertainty into your models and analyses to make them more reliable and robust.

Initializing

Exercise

Speed-accuracy trade-off

In the last video, you have seen there are two knobs you can tune to influence the performance of the random forests:

  • Number of decision trees in each forest.
  • Number of variables used for splitting within decision trees.

Increasing each of them might improve the accuracy of the imputation model, but it will also require more time to run. In this exercise, you will explore these ideas yourself by fitting missForest() to the biopics data twice with different settings. As you follow the instructions, pay attention to the errors you will be printing, and to the time the code takes to run.

Instructions 1/2

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  • 1
    • Run missForest() on biopics with 5 decision trees and 2 variables used for splitting and print the imputation error.
  • 2
    • Run missForest() on biopics with 50 decision trees and 6 variables used for splitting and print the imputation error.