uji t untuk MAR: persiapan data

Kerja bagus dalam mengklasifikasikan mekanisme data hilang pada latihan sebelumnya! Dari ketiganya, MAR bisa dibilang yang paling penting untuk dideteksi, karena banyak metode imputasi mengasumsikan data bersifat MAR. Oleh karena itu, latihan ini akan berfokus pada pengujian MAR.

Anda akan bekerja dengan himpunan data biopics yang sudah familier. Tujuannya adalah menguji apakah jumlah nilai yang hilang pada earnings berbeda menurut gender subjek. Pada latihan ini, Anda hanya akan menyiapkan data untuk uji t. Pertama, Anda akan membuat variabel dummy yang menandai keberadaan data hilang pada earnings. Lalu, Anda akan memecahnya per gender dengan terlebih dahulu menyaring data untuk mempertahankan salah satu gender, kemudian menarik (pull) variabel dummy tersebut. Untuk penyaringan, akan membantu jika Anda mencetak head() dari biopics di konsol dan memeriksa variabel gender.

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

Petunjuk latihan

Tambahkan variabel baru ke biopics bernama missing_earnings yang bernilai TRUE jika earnings hilang dan FALSE jika tidak.
Buat sebuah vektor nilai missing_earnings untuk laki-laki dan tetapkan ke missing_earnings_males.
Buat sebuah vektor nilai missing_earnings untuk perempuan dan tetapkan ke missing_earnings_females.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a dummy variable for missing earnings
biopics <- biopics %>% 
  ___(missing_earnings = ___(___))

# Pull the missing earnings dummy for males
missing_earnings_males <- biopics %>% 
  ___(___) %>% 
  ___(___)

# Pull the missing earnings dummy for females
missing_earnings_females <- biopics %>% 
  ___(___) %>% 
  ___(___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus 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: Data hilang: apa saja yang bisa salah Exercise 2: Regresi linear dengan data tidak lengkap Exercise 3: Menganalisis keluaran regresi Exercise 4: Membandingkan model Exercise 5: Mekanisme data hilang Exercise 6: Mengenali mekanisme data hilang Exercise 7: uji t untuk MAR: persiapan data

Latihan Saat Ini

Exercise 8: uji t untuk MAR: interpretasi Exercise 9: Memvisualisasikan pola data hilang Exercise 10: Plot agregasi Exercise 11: Spine plot Exercise 12: Plot mosaik

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: 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