Menggunakan matriks prediktor

Keputusan penting saat menggunakan imputasi berbasis model adalah menentukan variabel mana yang harus disertakan sebagai prediktor, dan dalam model yang mana. Dalam mice(), hal ini diatur oleh matriks prediktor dan, secara bawaan, semua variabel digunakan untuk mengimputasi variabel lainnya.

Jika terdapat banyak variabel dalam data atau waktu Anda terbatas untuk melakukan pemilihan model yang tepat, Anda dapat menggunakan fungsionalitas mice untuk membuat matriks prediktor berdasarkan korelasi antarkomponen variabel. Matriks ini kemudian dapat diberikan ke mice(). Pada latihan ini, Anda akan mempraktikkan hal tersebut: pertama, Anda akan membangun matriks prediktor sehingga setiap variabel akan diimputasi menggunakan variabel yang paling berkorelasi dengannya; lalu, Anda akan memberikan matriks prediktor tersebut ke fungsi imputasi. Mari coba pemilihan model sederhana ini!

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create predictor matrix with minimum correlation of 0.1
pred_mat <- ___(biopics, mincor = ___)

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: 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: Imputasi berganda dengan bootstrapping Exercise 2: Membungkus imputasi & pemodelan ke dalam sebuah fungsi Exercise 3: Menjalankan bootstrap Exercise 4: Interval kepercayaan dengan bootstrapping Exercise 5: Multiple imputation dengan persamaan berantai Exercise 6: Alur mice: mice - with - pool Exercise 7: Memilih model bawaan Exercise 8: Menggunakan matriks prediktor

Latihan Saat Ini

Exercise 9: Menggabungkan semuanya Exercise 10: Menganalisis pola data hilang Exercise 11: Mengimputasi dan memeriksa keluaran Exercise 12: Inferensi dengan data imputasi Exercise 13: Catatan penutup