Alur mice: mice - with - pool

Multiple imputation by chained equations (MICE) memungkinkan kita mengestimasi ketidakpastian dari imputasi dengan mengimputasi suatu himpunan data beberapa kali menggunakan imputasi berbasis model, sambil mengambil sampel dari sebaran bersyarat. Dengan cara ini, setiap himpunan data terimputasi akan sedikit berbeda. Kemudian, analisis dilakukan pada masing-masing himpunan data dan hasilnya digabungkan, menghasilkan besaran yang diminati beserta interval kepercayaan yang mencerminkan ketidakpastian imputasi.

Dalam latihan ini, Anda akan mempraktikkan alur MICE yang umum: mice() - with() - pool(). Anda akan melakukan analisis regresi pada data biopics untuk melihat jenis pekerjaan subjek, sub_type, yang terkait dengan pendapatan film tertinggi. Mari bermain dengan mice!

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

Petunjuk latihan

Muat paket mice dan imputasi biopics dengan mice() menggunakan 5 imputasi, lalu simpan hasilnya ke biopics_multiimp.
Sesuaikan model regresi linear yang memodelkan earnings menggunakan year dan sub_type pada setiap himpunan data terimputasi, lalu simpan hasilnya ke lm_multiimp.
Gabungkan model regresi yang disimpan di lm_multiimp, lalu simpan hasilnya ke lm_pooled.
Ringkas lm_pooled sehingga menghasilkan interval kepercayaan dengan tingkat kepercayaan 95%.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Load mice package
___

# Impute biopics with mice using 5 imputations
biopics_multiimp <- ___(___, m = ___, seed = 3108)

# Fit linear regression to each imputed data set 
lm_multiimp <- ___(___, ___)

# Pool and summarize regression results
lm_pooled <- ___(___)
___(___, conf.int = ___, conf.level = ___)

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: 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

Latihan Saat Ini

Exercise 7: Memilih model bawaan Exercise 8: Menggunakan matriks prediktor 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