Histogram van Gaussian Mixture plotten

Met histogrammen kun je een algemeen beeld krijgen van de verdeling van een mixturemodel. Uit een histogram kun je soms afleiden hoeveel componenten het heeft en of de aannames over de verdeling passend zijn.

Deze oefening maakt deel uit van de cursus

Mixture Models in R

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Gebruik ggplot() om het histogram te maken voor de gesimuleerde data mixture. Zet daarvoor eerst mixture om naar een data frame met een variabele x.
Gebruik daarna de functie geom_histogram() en geef aan dat de y-as de dichtheid moet zijn en dat het aantal bins 50 is.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Transform into a data frame
mixture <- ___(x = ___)

# Create histogram especifiying that is a density plot
mixture %>% 
  ggplot() + geom_histogram(aes(x = x, y = ___), bins = ___)

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Deze oefening maakt deel uit van de cursus

Mixture Models in R

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4.8+

Begin de cursus gratis

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: Introductie van modelgebaseerde clustering Exercise 2: Clustering-methoden Exercise 3: Verken geslachtsgegevens Exercise 4: Gaussiaanse verdeling Exercise 5: Steekproeven uit een Gaussische verdeling nemen Exercise 6: (niet zo goed) Schattingen van het gemiddelde en de sd Exercise 7: Gaussian mixturemodellen (GMM)Exercise 8: Simuleer een mengsel van twee Gaussische verdelingen Exercise 9: Histogram van Gaussian Mixture plotten

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Exercise 10: Mix van drie Gaussiaanse verdelingen

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 Mixture Models Exercise 2: Number of clusters Exercise 3: Number of parameters Exercise 4: Univariate Gaussian Mixture Models with flexmix Exercise 5: Univariate case with flexmix Exercise 6: Extracting Parameters for Univariate Case Exercise 7: Visualizing Univariate Gaussian Mixture Model Exercise 8: Compare the results Exercise 9: Bivariate Gaussian Mixture Models Exercise 10: Cross-term from covariance matrix Exercise 11: Parameters in the bivariate case Exercise 12: Bivariate Gaussian Mixture Models with flexmix Exercise 13: Fit the model with cross-terms Exercise 14: Get the components Exercise 15: Create the ellipses Exercise 16: Visualize the clusters

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`