Bootstrap'ı çalıştırma

Son egzersizde calc_gender_coef() fonksiyonunu yazdığın için harika iş çıkardın! Bu fonksiyon bir bootstrap örneklemi oluşturur, onu imputasyonla doldurur ve film konusunun kadın olmasının filmin gelirleri üzerindeki etkisini tanımlayan doğrusal regresyon katsayısını üretir.

Bu egzersizde, bu tür katsayıların bootstrap edilmiş bir dağılımını elde etmek için boot paketini kullanacaksın. Bu dağılımın yayılımı, imputasyondan kaynaklanan belirsizliği yansıtacaktır. Ayrıca, bootstrap edilmiş dağılımın tek seferlik imputasyon ve regresyondan nasıl farklılaştığını da göreceksin. Haydi bootstrap yapalım!

Bu egzersiz

R'de Atamaya Dayalı Eksik Veri Doldurma

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

boot paketini yükle.
biopics üzerinde bootstrapping çalıştır; statistic olarak calc_gender_coef'i geçir ve R'yi 50 olarak ayarla; sonucu boot_results'a ata.
Bootstrapping sonuçlarını print() ve plot() ile görüntüle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Load the boot library
___

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

# Print and plot bootstrapping results
___
___

Kodu Düzenle ve Çalıştır

Bu egzersiz

R'de Atamaya Dayalı Eksik Veri Doldurma

kursunun bir parçasıdır

AvançadoNível de habilidade

4.7+

Kursa Ücretsiz Başlayın

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: Bootstrap ile çoklu atama Exercise 2: Atama ve modellemeyi bir fonksiyona sarmalamak Exercise 3: Bootstrap'ı çalıştırma

Geçerli Egzersiz

Exercise 4: Bootstrap ile güven aralıkları Exercise 5: Zincirleme denklemlerle çoklu atama Exercise 6: mice akışı: mice - with - pool Exercise 7: Varsayılan modelleri seçmek Exercise 8: Tahminci matrisi kullanma Exercise 9: Hepsini bir araya getirmek Exercise 10: Eksik veri kalıplarını analiz etme Exercise 11: Sonuçları atama ve inceleme Exercise 12: Atanan verilerle çıkarım Exercise 13: Son notlar