De bootstrap uitvoeren

Goed gedaan met het schrijven van calc_gender_coef() in de vorige oefening! Deze functie maakt een bootstrap-steekproef, voert imputatie uit en geeft de lineaire regressiecoëfficiënt terug die het effect weergeeft van een vrouwelijke filmsubject op de opbrengst van de film.

In deze oefening gebruik je het boot-pakket om een gebootstrapte verdeling van zulke coëfficiënten te verkrijgen. De spreiding van deze verdeling vangt de onzekerheid door imputatie. Je bekijkt ook hoe de gebootstrapte verdeling verschilt van één keer imputeren en regressie draaien. Aan de slag met bootstrapping!

Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

Cursus bekijken

Oefeninstructies

Laad het boot-pakket.
Voer bootstrapping uit op biopics, geef calc_gender_coef door als statistic en zet R op 50; sla het resultaat op in boot_results.
print() en plot() de bootstrappingresultaten.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the boot library
___

# Run bootstrapping on biopics data
boot_results <- ___(___, statistic = ___, R = ___)

# Print and plot bootstrapping results
___
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

SkillTag.level.advancedSkillTag.label

4.7+

Begin de cursus gratis

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.

Exercise 1: Missing data: what can go wrong Exercise 2: Linear regression with incomplete data Exercise 3: Analyzing regression output Exercise 4: Comparing models Exercise 5: Missing data mechanisms Exercise 6: Recognizing missing data mechanisms Exercise 7: t-test for MAR: data preparation Exercise 8: t-test for MAR: interpretation Exercise 9: Visualizing missing data patterns Exercise 10: Aggregation plot Exercise 11: Spine plot Exercise 12: Mosaic plot

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.

Exercise 1: Mean imputation Exercise 2: Smelling the danger of mean imputation Exercise 3: Mean-imputing the temperature Exercise 4: Assessing imputation quality with margin plot Exercise 5: Hot-deck imputation Exercise 6: Vanilla hot-deck Exercise 7: Hot-deck tricks & tips I: imputing within domains Exercise 8: Hot-deck tricks & tips II: sorting by correlated variables Exercise 9: k-Nearest-Neighbors imputation Exercise 10: Choosing the number of neighbors Exercise 11: kNN tricks & tips I: weighting donors Exercise 12: kNN tricks & tips II: sorting variables

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.

Exercise 1: Model-based imputation approach Exercise 2: Linear regression imputation Exercise 3: Initializing missing values & iterating over variables Exercise 4: Detecting convergence Exercise 5: Replicating data variability Exercise 6: Logistic regression imputation Exercise 7: Drawing from conditional distribution Exercise 8: Model-based imputation with multiple variable types Exercise 9: Tree-based imputation Exercise 10: Imputing with random forests Exercise 11: Variable-wise imputation errors Exercise 12: Speed-accuracy trade-off

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.

Exercise 1: Meervoudige imputatie via bootstrapping Exercise 2: Imputatie en modelleren verpakken in een functie Exercise 3: De bootstrap uitvoeren

Huidige oefening

Exercise 4: Bootstrap-confidence-intervallen Exercise 5: Meervoudige imputatie met chained equations Exercise 6: De mice-flow: mice - with - pool Exercise 7: Standaardmodellen kiezen Exercise 8: Een predictormatrix gebruiken Exercise 9: Alles samenbrengen Exercise 10: Analyseren van patronen in missende data Exercise 11: Imputeren en resultaten inspecteren Exercise 12: Inferentie met geïmputeerde data Exercise 13: Slotopmerkingen