Hot-deck: trucchi e consigli II — ordinare per variabili correlate

Un altro trucco che può migliorare le prestazioni della hot-deck imputation è ordinare i dati in base a variabili correlate a quella che vuoi imputare.

Per esempio, in tutti i margin plot che hai disegnato di recente, hai visto che la temperatura dell’aria è fortemente correlata con la temperatura superficiale del mare, il che è del tutto sensato. Puoi sfruttare questa informazione per migliorare la tua hot-deck imputation. Se ordini prima i dati per sea_surface_temp, allora ogni valore imputato di air_temp proverrà da un donatore con una sea_surface_temp simile. Vediamo come funziona!

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Gestione dei dati mancanti con imputazioni in R

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Istruzioni dell'esercizio

Imputa con hot-deck i valori mancanti di air_temp nei dati tao, ordinando per sea_surface_temp, e assegna il risultato a tao_imp.
Crea un margin plot di air_temp vs sea_surface_temp.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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) %>% 
	___(___ = ___)

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Questo esercizio fa parte del corso

Gestione dei dati mancanti con imputazioni in R

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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: Imputazione con media Exercise 2: Capire i rischi della imputazione con la media Exercise 3: Imputare la temperatura con la media Exercise 4: Valutare la qualità dell'imputazione con il margin plot Exercise 5: Imputazione hot-deck Exercise 6: Hot-deck semplice Exercise 7: Hot-deck: trucchi e consigli I — imputazione per domini Exercise 8: Hot-deck: trucchi e consigli II — ordinare per variabili correlate

Esercizio in corso

Exercise 9: Imputazione con k-Nearest Neighbors Exercise 10: Scegliere il numero di vicini Exercise 11: kNN: trucchi e consigli I — pesare i donatori Exercise 12: Trucchi e consigli per kNN II: ordinare le variabili

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