Mengambil sampel dari distribusi kondisional

Memanggil predict() pada sebuah model akan selalu mengembalikan nilai yang sama untuk nilai prediktor yang sama. Ini menghasilkan keragaman yang kecil pada data imputasi. Untuk meningkatkannya, agar imputasi mereplikasi keragaman dari data asli, kita dapat mengambil sampel dari distribusi kondisional. Artinya, alih-alih selalu memprediksi 1 setiap kali model menghasilkan probabilitas lebih besar dari 0,5, kita dapat mengambil prediksi dari distribusi binomial yang ditentukan oleh probabilitas yang dikembalikan model.

Anda akan bekerja pada kode yang telah Anda tulis di latihan sebelumnya. Baris berikut telah dihapus:

  preds <- ifelse(preds >= 0.5, 1, 0)

Tugas Anda adalah menggantinya dengan pengambilan sampel dari distribusi binomial. Cukup satu baris kode!

Latihan ini adalah bagian dari kursus

Menangani Data Hilang dengan Imputasi di R

Petunjuk latihan

Timpa preds dengan mengambil sampel dari distribusi binomial.
Berikan panjang preds sebagai argumen pertama.
Atur size ke 1.
Atur prob ke probabilitas yang dikembalikan oleh model.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

 impute_logreg <- function(df, formula) {
  # Extract name of response variable
  imp_var <- as.character(formula[2])
  # Save locations where the response is missing
  missing_imp_var <- is.na(df[imp_var])
  # Fit logistic regression mode
  logreg_model <- glm(formula, data = df, family = binomial)
  # Predict the response
  preds <- predict(logreg_model, type = "response")
  # Sample the predictions from binomial distribution
  preds <- ___(___, size = ___, prob = ___)
  # Impute missing values with predictions
  df[missing_imp_var, imp_var] <- preds[missing_imp_var]
  return(df)
}

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: Pendekatan imputasi berbasis model Exercise 2: Imputasi regresi linear Exercise 3: Menginisialisasi nilai yang hilang & iterasi atas variabel Exercise 4: Mendeteksi konvergensi Exercise 5: Mereplikasi variabilitas data Exercise 6: Imputasi dengan regresi logistik Exercise 7: Mengambil sampel dari distribusi kondisional

Latihan Saat Ini

Exercise 8: Imputasi berbasis model dengan berbagai tipe variabel Exercise 9: Imputasi berbasis pohon Exercise 10: Imputasi dengan random forests Exercise 11: Kesalahan imputasi per variabel Exercise 12: Pertukaran kecepatan–akurasi

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