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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

Logistic regression imputation

A popular choice for imputing binary variables is logistic regression. Unfortunately, there is no function similar to impute_lm() that would do it. That's why you'll write such a function yourself!

Let's call the function impute_logreg(). Its first argument will be a data frame df, whose missing values have been initialized and only containing missing values in the column to be imputed. The second argument will be a formula for the logistic regression model.

The function will do the following:

  • Keep the locations of missing values.
  • Build the model.
  • Make predictions.
  • Replace missing values with predictions.

Don't worry about the line creating imp_var - this is just a way to extract the name of the column to impute from the formula. Let's do some functional programming!

Instructions

100 XP
  • Create a boolean mask for where df[imp_var] is missing and assign it to missing_imp_var.
  • Fit a logistic regression model using the formula and data that the function will get as arguments, while remembering to set the correct family to ensure a logistic regression is fit (pass it without quotation marks); assign the model to logreg_model.
  • Predict the response with the model and assign it to preds; remember to set the appropriate prediction type.
  • Use preds alongside missing_imp_var to impute missing values.