Eseguire il bootstrap

Ottimo lavoro con calc_gender_coef() nell'esercizio precedente! Questa funzione crea un campione bootstrap, lo imputa e restituisce il coefficiente della regressione lineare che descrive l'impatto dell'essere donna del soggetto del film sui ricavi del film.

In questo esercizio userai il pacchetto boot per ottenere una distribuzione bootstrap di tali coefficienti. L'ampiezza di questa distribuzione catturerà l'incertezza dovuta all'imputazione. Vedrai anche come la distribuzione bootstrap differisce da un'unica imputazione seguita da una regressione. Pronti a fare un po' di bootstrapping!

Questo esercizio fa parte del corso

Gestione dei dati mancanti con imputazioni in R

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Istruzioni dell'esercizio

Carica il pacchetto boot.
Esegui il bootstrapping su biopics, passando calc_gender_coef come statistic e imposta R a 50; assegna il risultato a boot_results.
Usa print() e plot() per visualizzare i risultati del bootstrapping.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the boot library
___

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

# Print and plot bootstrapping results
___
___

Modifica ed esegui il codice

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: 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: Imputazione multipla con bootstrapping Exercise 2: Racchiudere imputazione e modellazione in una funzione Exercise 3: Eseguire il bootstrap

Esercizio in corso

Exercise 4: Intervalli di confidenza con bootstrapping Exercise 5: Imputazione multipla con equazioni a catena Exercise 6: Il flusso di mice: mice - with - pool Exercise 7: Scelta dei modelli predefiniti Exercise 8: Uso della matrice dei predittori Exercise 9: Mettiamo tutto insieme Exercise 10: Analizzare i pattern di dati mancanti Exercise 11: Imputazione e ispezione degli esiti Exercise 12: Inferenza con dati imputati Exercise 13: Considerazioni finali