Likelihood-ratio-toets

Deze oefening introduceert een nieuw concept dat we nog niet hebben behandeld, maar ik denk dat je er klaar voor bent. Likelihood-ratio-toetsen worden gebruikt om twee modellen te vergelijken. Ze vertellen ons of het ene model de data beter past dan het andere. Je kunt dit uitvoeren met het commando lrtest(), dat twee verschillende modelobjecten als invoer neemt. We kunnen het gebruiken om te testen of choc_m2, met een lineaire prijscoëfficiënt, de data beter past dan choc_m3, waarin we prijs als factor hebben behandeld. Die modellen zijn al geladen.

Deze oefening maakt deel uit van de cursus

Keuzemodellering voor marketing in R

Cursus bekijken

Oefeninstructies

Voer een likelihood-ratio-toets uit om de modellen choc_m2 en choc_m3 te vergelijken.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# likelihood ratio test comparing two models

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Keuzemodellering voor marketing in R

SkillTag.level.advancedSkillTag.label

4.8+

29 reviews

Begin de cursus gratis

Our goal for this chapter is to get you through the entire choice modeling process as quickly as possible, so that you get a broad understanding of what we can do with choice models and how the choice modeling process works. The main idea here is that we can use a choice model to understand how customers' product choices depend on the features of those products. Do sportscar buyers prefer manual transmissions to automatic? By how much? In order to give you an overview, we will skip over many of the details. In later chapters, we will go back and cover important issues in preparing data, specifying and interpreting models and reporting your findings, so that you are fully prepared to use these methods with your own choice data.

Exercise 1: Why choice?Exercise 2: Choice data Exercise 3: Inspecting choice data Exercise 4: Finding the levels of a factor Exercise 5: Inspecting a choice observation Exercise 6: What did people choose?Exercise 7: Fitting and interpreting a choice model Exercise 8: Fitting a choice model Exercise 9: Interpreting parameters Exercise 10: Using choice models to make decisions Exercise 11: Predicting choice shares Exercise 12: Plotting choice shares

There are many different places to get choice data and different ways it can be formatted. In this chapter, we will take data that is provided in several alternative formats and learn how to get it into shape for choice modeling. We will also discuss how you can build a survey to collect your own choice data.

Exercise 1: Assembling choice data Exercise 2: How many choices do we have?Exercise 3: Attributes and levels Exercise 4: Inspect a single choice Exercise 5: Converting from wide to long Exercise 6: Convert from wide to long Exercise 7: Sort the long data Exercise 8: Change the Selection variable Exercise 9: Choice data in two files Exercise 10: Inspect a choice in two files Exercise 11: Merging two files Exercise 12: Visualizing choice data Exercise 13: What types of chocolate do people choose?Exercise 14: Do people choose lower prices?Exercise 15: Designing a conjoint survey

In this chapter, we take deeper dive into estimating choice models. To give you a foundation for thinking about choice models, we will focus on how the multinomial logit model converts the product features into a prediction for what the decision maker will choose. This will give you a framework for making decisions about which features to include in your model.

Exercise 1: Keuzemodellen – onder de motorkap Exercise 2: Maak een mlogit.data-object Exercise 3: Pas een keuzemodel toe Exercise 4: Verwijder de intercept Exercise 5: Parameters van keuzemodellen interpreteren Exercise 6: Welke chocolademerk heeft de voorkeur?Exercise 7: Bereidheid om te betalen berekenen Exercise 8: Price als factor Exercise 9: Likelihood-ratio-toets

Huidige oefening

Exercise 10: Intercepters en interacties Exercise 11: Interactie tussen merk en type Exercise 12: Interactie tussen prijs en Trial Exercise 13: Aandelen voorspellen Exercise 14: Aandelen voorspellen voor het racer-segment Exercise 15: Voorspel aandelen voor chocoladerepen Exercise 16: Plot aandelen voor chocoladerepen

Different people have different tastes and preferences. This seems intuitively obvious, but there is also extensive research in marketing showing that this is true. This chapter covers choice models where we assume that different decision makers have different preferences that influence their choices. When our models recognize that different consumers have different preferences, they tend to make larger share predictions for niche products that appeal to a subset of consumers. Hierarchical models are used in most commercial choice modeling applications, so it is important to understand how they work.

Exercise 1: What is a hierarchical choice model?Exercise 2: How many people answered the chocolate suvey?Exercise 3: Chocolate model with random price coefficient Exercise 4: Heterogeneity in preferences for other features Exercise 5: Setting effects codes Exercise 6: Chocolate model with all coefficients random Exercise 7: Interpreting hierarchical model parameters Exercise 8: How do people value white chocolate?Exercise 9: Predicting shares with hierarchical models Exercise 10: Predicting shares for chocolates Exercise 11: Goodbye and good luck!