Prediksi berbatas

Fungsi glm() digunakan karena Anda memerlukan fungsi respons yang membatasi prediksi model antara nol dan satu. Mari ilustrasikan efek ini dalam sebuah grafik. Namun kali ini Anda perlu menggambar kurva (bukan garis lurus)!

Anda mulai dengan memplot hubungan HOPPINESS ~ price.ratio. Anda dapat menambahkan fungsi logistik ke plot dengan menggunakan curve(). Fungsi curve() digunakan untuk mengevaluasi fungsi lain pada titik data x. Di sini, fungsi tersebut adalah predict()! Fungsi predict() mengambil koefisien dari logistic.model untuk membuat prediksi atas beberapa nilai yang disediakan dalam objek data frame. Kuncinya adalah menetapkan price.ratio = x dalam argumen data.frame. Ini akan memasangkan kurva melalui nilai data yang diprediksi.

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

Petunjuk latihan

Tampilkan hubungan antara HOPPINESS dan price.ratio dengan menggunakan fungsi plot().
Peroleh probabilitas pembelian untuk Hoppiness dengan menerapkan fungsi predict() pada logistic.model dengan price.ratio dalam argumen data frame.
Pasangkan kurva melalui nilai data yang diprediksi dengan menggunakan fungsi curve().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot HOPPINESS against price.ratio
___(___, data = choice.data)

# Add the logistic response function
___(___(___, data.frame(___), type = "response"), add = TRUE)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membangun Model Respons di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

The first chapter introduces you to the basic principles and concepts of market response models. Here, you will learn how to build simple response models for product sales. In addition, you will learn about the theoretical and practical differences between linear and non-linear models for sales responses.

Exercise 1: Fundamentals of market response models Exercise 2: Retail sales Exercise 3: Understanding sales Exercise 4: Linear response models Exercise 5: A linear response model for sales Exercise 6: Making predictions Exercise 7: Predictive performance Exercise 8: Nonlinear response models Exercise 9: Linearizing nonlinear functions Exercise 10: What 's the value added?

An effective marketing strategy combines all the tools available to communicate the benefits of a product. The key is crafting the right mix of these tools to achieve sales increases and market share goals. In the second chapter, you will learn how to incorporate the effects of advertising and promotion in your sales-response model and how to identify the marketing strategy that is most likely to succeed.

Exercise 1: Model extension part 1: Dummy variables Exercise 2: Understanding dummy variables Exercise 3: The effect of display on sales Exercise 4: The effect of multiple dummies on sales Exercise 5: What about price?Exercise 6: Model extensions part 2: Dynamic variables Exercise 7: How to lag?Exercise 8: Adding lagged price effects Exercise 9: More lags Exercise 10: What's the value added?Exercise 11: How many extensions are needed?Exercise 12: Summarizing the model Exercise 13: Unnecessary predictors Exercise 14: Dropping predictors Exercise 15: Eliminating predictors

A company can only be successful in the market if its products have a competitive advantage over those of its rivals. To develop an effective marketing strategy in a competitive environment, it is essential to understand the interrelationship between marketing activity and customer behavior. In this chapter, you will learn how to explain the effects of temporary price changes on customer brand choice by employing logistic and probit response models.

Exercise 1: Model untuk permintaan individual Exercise 2: Pembelian pelanggan Exercise 3: Meringkas data Exercise 4: Persaingan Exercise 5: Model probabilitas linear untuk permintaan bir Exercise 6: Model respons logistik Exercise 7: Model logistik untuk permintaan bir Exercise 8: Prediksi berbatas

Latihan Saat Ini

Exercise 9: Rata-rata efek marginal Exercise 10: Plot efek Exercise 11: Model respons probit Exercise 12: Model probit untuk permintaan bir Exercise 13: Logistik vs. probit Exercise 14: Perbandingan model

The main goal of response modeling is to enable marketers to not only see a payoff for their actions today, but also tomorrow. In order to view this future payoff, a simple but reliable statistical model is required. In this last chapter, you will learn how to evaluate the predictive performance of logistic response models.

Exercise 1: Model selection Exercise 2: Extending the logistic response model Exercise 3: Summarizing the model Exercise 4: The deviance principle Exercise 5: Eliminating predictors Exercise 6: Predictive performance Exercise 7: Classifications Exercise 8: Model confusion Exercise 9: ROC curves Exercise 10: Model validation Exercise 11: Subsetting Exercise 12: Model training Exercise 13: Out-of-sample testing Exercise 14: Wrapping it up