Hot-deck tips & tricks II: sorteren op gecorreleerde variabelen

Een andere truc die de prestaties van hot-deck-imputatie kan verbeteren, is de gegevens sorteren op variabelen die correleren met de variabele die je wilt imputeren.

In alle marginplots die je onlangs hebt gemaakt, heb je bijvoorbeeld gezien dat luchttemperatuur sterk correleert met de zeewatertemperatuur, wat heel logisch is. Die kennis kun je benutten om je hot-deck-imputatie te verbeteren. Als je de gegevens eerst sorteert op sea_surface_temp, komt elke geïmuteerde air_temp-waarde van een donor met een vergelijkbare sea_surface_temp. Laten we kijken hoe dat werkt!

Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

Cursus bekijken

Oefeninstructies

Voer hot-deck-imputatie uit voor de missende waarden in air_temp in de tao-gegevens, gesorteerd op sea_surface_temp, en sla het resultaat op in tao_imp.
Maak een marginplot van air_temp versus sea_surface_temp.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Hot-deck-impute air_temp in tao ordering by sea_surface_temp
tao_imp <- ___(___, ___ = ___, ___ = ___)

# Draw a margin plot of air_temp vs sea_surface_temp
tao_imp %>% 
	select(air_temp, sea_surface_temp, air_temp_imp) %>% 
	___(___ = ___)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

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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: Gemiddelde-imputatie Exercise 2: De gevaren van mean-imputatie ruiken Exercise 3: Gemiddelde-imputatie voor de temperatuur Exercise 4: De imputatiekwaliteit beoordelen met een margeplot Exercise 5: Hot-deck-imputatie Exercise 6: Standaard hot-deck Exercise 7: Hot-deck tips & tricks I: imputeren binnen domeinen Exercise 8: Hot-deck tips & tricks II: sorteren op gecorreleerde variabelen

Huidige oefening

Exercise 9: k-Nearest-Neighbors-imputatie Exercise 10: Het aantal buren kiezen Exercise 11: kNN tips & tricks I: donoren wegen Exercise 12: kNN tips & tricks II: variabelen sorteren

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: Multiple imputation by bootstrapping Exercise 2: Wrapping imputation & modeling in a function Exercise 3: Running the bootstrap Exercise 4: Bootstrapping confidence intervals Exercise 5: Multiple imputation by chained equations Exercise 6: The mice flow: mice - with - pool Exercise 7: Choosing default models Exercise 8: Using predictor matrix Exercise 9: Putting it all together Exercise 10: Analyzing missing data patterns Exercise 11: Imputing and inspecting outcomes Exercise 12: Inference with imputed data Exercise 13: Final remarks