Maximization function
We saw that the EM algorithm is an iterative method between two steps: the expectation and the maximization. In the last exercise, you created the expectation function. Now, create the maximization function which takes the data frame with the probabilities and outputs the estimations of the means and proportions.
Questo esercizio fa parte del corso
Mixture Models in R
Istruzioni dell'esercizio
Create the function maximization by completing the sample code.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
maximization <- function(___){
means_estimates <- data_with_probs %>%
summarise(mean_1 = sum(x * ___) / ___(prob_cluster1),
mean_2 = sum(x * ___) / ___(prob_cluster2)) %>%
as.numeric()
props_estimates <- data_with_probs %>%
summarise(proportion_1 = ___(prob_cluster1),
proportion_2 = 1 - ___) %>%
as.numeric()
list(means_estimates, props_estimates)
}