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.
Este exercício faz parte do curso
Mixture Models in R
Instruções do exercício
Create the function maximization by completing the sample code.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
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)
}