Trik & kiat hot-deck I: imputasi di dalam domain

Salah satu trik yang dapat membantu ketika imputasi hot-deck merusak hubungan antarkomponen variabel adalah melakukan imputasi di dalam domain. Artinya, jika variabel yang akan diimputasi berkorelasi dengan variabel kategorikal lain, Anda dapat menjalankan hot-deck secara terpisah untuk setiap kategorinya.

Sebagai contoh, Anda mungkin memperkirakan suhu udara bergantung pada waktu, mengingat kita melihat rata-rata suhu meningkat akibat pemanasan global. Indikator waktu yang tersedia pada data tao adalah variabel kategorikal, year. Mari kita periksa terlebih dahulu apakah rata-rata suhu udara berbeda pada masing-masing dari dua tahun yang dikaji, lalu jalankan hot-deck dalam domain per tahun. Terakhir, Anda akan membuat margin plot lagi untuk menilai kinerja imputasi.

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

Petunjuk latihan

Hitung mean air_temp untuk setiap tahun, beri nama hasilnya average_air_temp dengan mengecualikan NA dalam perhitungan mean.
Imputasi nilai yang hilang pada air_temp dalam data tao di dalam domain year menggunakan imputasi hot-deck dan simpan hasilnya ke tao_imp.
Buat margin plot air_temp vs sea_surface_temp; ingat untuk menyertakan air_temp_imp dalam variabel yang Anda berikan ke fungsi pemetaan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate mean air_temp per year
tao %>% 
	group_by(___) %>% 
	summarize(average_air_temp = mean(___, na.rm = ___))

# Hot-deck-impute air_temp in tao by year domain
tao_imp <- ___(___, variable = ___, ___ = ___)

# Draw a margin plot of air_temp vs sea_surface_temp
tao_imp %>% 
	select(___, ___, ___) %>% 
	marginplot(___ = ___)

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: 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 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

Latihan Saat Ini

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