Parameter für den univariaten Fall extrahieren

In der vorherigen Übung hast du mit flexmix ein univariates gaußsches Mischungsmodell gefittet und im Objekt fit_mix_example gespeichert. Ziel dieses Moduls ist es, die Ergebnisse zu analysieren, indem du die geschätzten Parameter extrahierst.

Diese Übung ist Teil des Kurses

<Kurs>Mischungsmodelle in R</Kurs>

Übungsanweisungen

Verwende die Funktion prior(), um die Anteile jedes Clusters zu erhalten.
Verwende die Funktion parameters() und das entsprechende Argument, um die Parameter für jede Komponente zu erhalten.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

proportions <- ___(fit_mix_example)
comp_1 <- parameters(___, component = ___)
comp_2 <- parameters(___, component = ___)
comp_3 <- parameters(___, component = ___)

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Diese Übung ist Teil des Kurses

<Kurs>Mischungsmodelle in R</Kurs>

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4.7+

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In this chapter, you will be introduced to fundamental concepts in model-based clustering and how this approach differs from other clustering techniques. You will learn the generating process of Gaussian Mixture Models as well as how to visualize the clusters.

Exercise 1: Introduction to model-based clustering Exercise 2: Clustering approaches Exercise 3: Explore gender data Exercise 4: Gaussian distribution Exercise 5: Sampling a Gaussian distribution Exercise 6: (not so good) Estimations of the mean and sd Exercise 7: Gaussian mixture models (GMM)Exercise 8: Simulate a mixture of two Gaussian distributions Exercise 9: Plot histogram of Gaussian Mixture Exercise 10: Mixture of three Gaussian distributions

In this chapter, you will be introduced to the main structure of Mixture Models, how to address different data with this approach and how to estimate the parameters involved. To accomplish the estimation, you will learn an iterative method called Expectation-Maximization algorithm.

Exercise 1: Structure of mixture models Exercise 2: Which probability distribution?Exercise 3: Handwritten digits dataset Exercise 4: Parameters estimation Exercise 5: Estimation given the probabilities Exercise 6: Calculating the probabilities Exercise 7: EM algorithm Exercise 8: Expectation function Exercise 9: Maximization function Exercise 10: Apply the two steps Exercise 11: Plot the estimated clusters

This chapter shows how to fit Gaussian Mixture Models in 1 and 2 dimensions with `flexmix` package. The data used is formed by 10.000 observations of people with their weight, height, body mass index and informed gender.

Exercise 1: Univariate Gaussian-Mischungsmodelle Exercise 2: Anzahl der Cluster Exercise 3: Anzahl der Parameter Exercise 4: Univariate Gaußsche Mischungsmodelle mit flexmix Exercise 5: Univariater Fall mit flexmix Exercise 6: Parameter für den univariaten Fall extrahieren

Aktuelle Übung

Exercise 7: Univariates Gaußsches Mischmodell visualisieren Exercise 8: Vergleiche die Ergebnisse Exercise 9: Bivariate Gaußsche Mischungsmodelle Exercise 10: Kreuzterm aus der Kovarianzmatrix Exercise 11: Parameter im bivariaten Fall Exercise 12: Bivariate Gaußsche Mischungsmodelle mit flexmix Exercise 13: Modell mit Kreuztermen fitten Exercise 14: Die Komponenten abrufen Exercise 15: Erzeuge die Ellipsen Exercise 16: Visualisiere die Cluster

In this module, you will learn how Mixture Models extends to consider probability distributions different from the Gaussian and how these models are fitted with `flexmix`. The datasets used are handwritten digits images and the number of crimes in Chicago city. For the first dataset you will find clusters that summarize the handwritten digits and for the second dataset, you will find clusters of communities where is more or less dangerous to live in.

Exercise 1: Bernoulli Mixture Models Exercise 2: Binary images Exercise 3: How many values?Exercise 4: Bernoulli Mixture Models with flexmix Exercise 5: Handwritten digits with `flexmix`Exercise 6: Poisson Mixture Models Exercise 7: Discover the lambda Exercise 8: Sample from Poisson distribution Exercise 9: Poisson Mixture Models with flexmix Exercise 10: Crimes data with `flexmix`