Menggunakan broom

Banyak tugas pemrograman menjadi lebih mudah jika Anda menyimpan semua data di dalam data frame. Hal ini semakin benar jika Anda pengguna setia tidyverse, di mana dplyr dan ggplot2 mengharuskan Anda menggunakan data frame. Paket broom menyediakan fungsi-fungsi yang menguraikan model menjadi tiga data frame: satu untuk elemen tingkat koefisien (koefisien itu sendiri, serta p-value untuk tiap koefisien), elemen tingkat observasi (seperti nilai taksiran/fitted dan residual), serta elemen tingkat model (kebanyakan metrik kinerja).

Fungsi-fungsi dalam broom bersifat generik. Artinya, fungsi-fungsi ini bekerja dengan banyak jenis model, tidak hanya objek model regresi linear. Fungsi-fungsi tersebut juga bekerja dengan objek model regresi logistik (seperti yang akan Anda lihat di Bab 4), dan banyak jenis model lainnya.

mdl_price_vs_conv tersedia dan broom sudah dimuat.

Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Get the coefficient-level elements of the model
___

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Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

Exercise 1: A tale of two variables Exercise 2: Which one is the response variable?Exercise 3: Visualizing two variables Exercise 4: Fitting a linear regression Exercise 5: Estimate the intercept Exercise 6: Estimate the slope Exercise 7: Linear regression with lm()Exercise 8: Categorical explanatory variables Exercise 9: Visualizing numeric vs. categorical Exercise 10: Calculating means by category Exercise 11: lm() with a categorical explanatory variable

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Exercise 1: Membuat prediksi Exercise 2: Memprediksi harga rumah Exercise 3: Memvisualisasikan prediksi Exercise 4: Batasan prediksi Exercise 5: Bekerja dengan objek model Exercise 6: Mengekstrak elemen model Exercise 7: Memprediksi harga rumah secara manual Exercise 8: Menggunakan broom

Latihan Saat Ini

Exercise 9: Regresi ke rerata Exercise 10: Home run!Exercise 11: Memplot imbal hasil portofolio berurutan Exercise 12: Memodelkan return berurutan Exercise 13: Mentransformasikan variabel Exercise 14: Mentransformasi variabel penjelas Exercise 15: Mentransformasi variabel respon juga

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.

Exercise 1: Quantifying model fit Exercise 2: Coefficient of determination Exercise 3: Residual standard error Exercise 4: Visualizing model fit Exercise 5: Residuals vs. fitted values Exercise 6: Q-Q plot of residuals Exercise 7: Scale-location Exercise 8: Drawing diagnostic plots Exercise 9: Outliers, leverage, and influence Exercise 10: Leverage Exercise 11: Influence Exercise 12: Extracting leverage and influence

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Exercise 1: Why you need logistic regression Exercise 2: Exploring the explanatory variables Exercise 3: Visualizing linear and logistic models Exercise 4: Logistic regression with glm()Exercise 5: Predictions and odds ratios Exercise 6: Probabilities Exercise 7: Most likely outcome Exercise 8: Odds ratio Exercise 9: Log odds ratio Exercise 10: Quantifying logistic regression fit Exercise 11: Calculating the confusion matrix Exercise 12: Measuring logistic model performance Exercise 13: Accuracy, sensitivity, specificity Exercise 14: Congratulations