Définir des codes d’effets

Dans cet exercice, nous allons modifier le data frame chocolate afin que les contrastes des facteurs Brand et Type soient définis en codes d’effets. Les données chocolate sont déjà chargées et j’ai écrit le code pour modifier le facteur Brand. À vous de copier ce code et de l’adapter à la variable Type.

Cet exercice fait partie du cours

<cours>Modélisation des choix pour le marketing en R</cours>

Instructions de l’exercice

Copiez les trois lignes de code que j’ai écrites pour changer les contrastes de la variable Brand.
Remplacez chocolate$Brand par chocolate$Type dans le code copié.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# set the contrasts for Brand to effects code
contrasts(chocolate$Brand) <- contr.sum(levels(chocolate$Brand))
dimnames(contrasts(chocolate$Brand))[[2]] <- levels(chocolate$Brand)[1:4]
contrasts(chocolate$Brand)

# set the contrasts for Type to effects code

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Modélisation des choix pour le marketing en R</cours>

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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: Choice models - under the hood Exercise 2: Create mlogit.data object Exercise 3: Fit a choice model Exercise 4: Remove the intercept Exercise 5: Interpreting choice model parameters Exercise 6: Which chocolate brand is most preferred?Exercise 7: Computing willingness-to-pay Exercise 8: Price as a factor Exercise 9: Likelihood-ratio test Exercise 10: Intercepts and interactions Exercise 11: Interaction between brand and type Exercise 12: Interaction between price and trial Exercise 13: Predicting shares Exercise 14: Predicting shares for the racer segment Exercise 15: Predict shares for chocolate bars Exercise 16: Plot shares for chocolate bars

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: Qu’est-ce qu’un modèle de choix hiérarchique ?Exercise 2: Combien de personnes ont répondu à l’enquête sur le chocolat ?Exercise 3: Modèle de chocolat avec coefficient de prix aléatoire Exercise 4: Hétérogénéité des préférences pour d’autres caractéristiques Exercise 5: Définir des codes d’effets

Exercice actuel

Exercise 6: Modèle chocolat avec tous les coefficients aléatoires Exercise 7: Interpréter les paramètres d’un modèle hiérarchique Exercise 8: Comment les personnes évaluent-elles le chocolat blanc ?Exercise 9: Prédire les parts avec des modèles hiérarchiques Exercise 10: Prédire les parts pour des chocolats Exercise 11: Au revoir et bonne chance !