1. Aprender
    2. /
  2. Cursos
    3. /
  3. Handling Missing Data with Imputations in R
  • 1

    The Problem of Missing Data

    Gratuito

    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

Exercício

Initializing missing values & iterating over variables

As you have just seen, running impute_lm() might not fill-in all the missing values. To ensure you impute all of them, you should initialize the missing values with a simple method, such as the hot-deck imputation you learned about in the previous chapter, which simply feeds forward the last observed value.

Moreover, a single imputation is usually not enough. It is based on the basic initialized values and could be biased. A proper approach is to iterate over the variables, imputing them one at a time in the locations where they were originally missing.

In this exercise, you will first initialize the missing values with hot-deck imputation and then loop five times over air_temp and humidity from the tao data to impute them with linear regression. Let's get to it!

Instruções

100 XP
  • Initialize the missing values with the hotdeck() imputation.
  • Create a boolean mask for where humidity was originally missing and assign it to missing_humidity.
  • Inside the for-loop, set the humidity in tao_imp to NA in places where it was originally missing using the boolean mask you have created.
  • Inside the for-loop, impute humidity in tao_imp with linear regression, using year, latitude, sea_surface_temp and air_temp as predictors and re-assign the result to tao_imp.