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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

Variable-wise imputation errors

In the previous exercise you have extracted the estimated imputation errors from missForest's output. This gave you two numbers:

  • the normalized root mean squared error (NRMSE) for all continuous variables;
  • the proportion of falsely classified entries (PFC) for all categorical variables.

However, it could well be that the imputation model performs great for one continuous variable and poor for another! To diagnose such cases, it is enough to tell missForest to produce variable-wise error estimates. This is done by setting the variablewise argument to TRUE.

The biopics data and missForest package have already been loaded for you, so let's take a closer look at the errors!

Instructions 1/3

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  • Use missForest to impute the biopics data so that variable-wise error estimates are computed.