Het aantal buren kiezen

k-Nearest-Neighbors- (of kNN-)imputatie vult ontbrekende waarden in een observatie op basis van de waarden uit de k andere, meest vergelijkbare observaties. Het aantal van deze vergelijkbare observaties, de zogeheten buren, dat je meeneemt, is een parameter die je vooraf moet kiezen.

Hoe kies je k? Een manier is om verschillende waarden te proberen en te bekijken hoe die de relatie tussen de geïmputeerde en geobserveerde data beïnvloeden.

Laten we humidity in de tao-data imputeren met drie verschillende waarden van k en kijken hoe de geïmputeerde waarden passen bij de relatie tussen humidity en sea_surface_temp.

Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Impute humidity using 30 neighbors
tao_imp <- ___(tao, k = ___, variable = ___)

# Draw a margin plot of sea_surface_temp vs humidity
tao_imp %>% 
	select(sea_surface_temp, humidity, humidity_imp) %>% 
	___(delimiter = "imp", main = "k = 30")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Omgaan met missende data met imputaties in R

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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 Exercise 9: k-Nearest-Neighbors-imputatie Exercise 10: Het aantal buren kiezen

Huidige oefening

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