Imputasi rata-rata untuk temperatur

Imputasi rata-rata bisa berisiko. Jika variabel yang Anda imputasi dengan rata-rata berkorelasi dengan variabel lain, korelasi tersebut bisa rusak oleh nilai hasil imputasi. Anda sudah melihat tanda-tandanya pada latihan sebelumnya saat menganalisis variabel air_temp.

Untuk mengetahui apakah kekhawatiran ini beralasan, pada latihan ini Anda akan melakukan imputasi rata-rata pada air_temp, sambil membuat indikator biner untuk menandai lokasi nilai yang diimputasi. Ini akan berguna pada latihan berikutnya saat Anda menilai kinerja imputasi. Mari kita isi nilai-nilai yang hilang itu!

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

Petunjuk latihan

Dalam pipeline yang memodifikasi tao, buat variabel baru bernama air_temp_imp yang bernilai TRUE jika air_temp hilang dan FALSE jika tidak.
Masih dalam pipeline yang sama, timpa air_temp dengan nilai rata-ratanya sendiri setiap kali nilainya hilang dan biarkan tanpa perubahan jika tidak, lalu simpan hasilnya sebagai tao_imp.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

tao_imp <- tao %>% 
  # Create a binary indicator for missing values in air_temp
  ___(air_temp_imp = ifelse(___(___), ___, ___)) %>% 
  # Impute air_temp with its mean
  ___(air_temp = ifelse(___(___), ___(___, na.rm = ___), ___))

# Print the first 10 rows of tao_imp
head(tao_imp, 10)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

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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: 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: Imputasi mean Exercise 2: Mencium bahaya imputasi rata-rata Exercise 3: Imputasi rata-rata untuk temperatur

Latihan Saat Ini

Exercise 4: Menilai kualitas imputasi dengan margin plot Exercise 5: Imputasi hot-deck Exercise 6: Hot-deck standar Exercise 7: Trik & kiat hot-deck I: imputasi di dalam domain Exercise 8: Trik & kiat hot-deck II: mengurutkan berdasarkan variabel berkorelasi Exercise 9: Imputasi k-Nearest-Neighbors Exercise 10: Memilih jumlah tetangga Exercise 11: Trik & kiat kNN I: pembobotan donor Exercise 12: kNN: kiat & trik II — mengurutkan variabel

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