em.mixnorm: EM-Algorithm for Normal Distribution

In JackStat/CompPack: Collection of Computational Statistic Tools

Description

The Expectation-Maximization (EM) algorithm is and itterative method for locating the maximum estimate for the parameters of a mixture of normal distributions. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates

Usage

  em.mixnorm(x, k)

Arguments

x	the data.
k	estimate of the number of mixtures.

Value

itterations number of cycles the program used to calculate the estimated means, standard devaitions and probabilities.

means estimated mean of each mixture.

stddevs estimated standard deviation of each mixture.

probs estimated probabilities of each mixture.

Author(s)

Tyler Hunt [e-mail]

Examples

x=mixnorm(100, c(.50, .15, .35), c(-3, 0, 3), c(3,3,3))
em.mixnorm(x,3)

JackStat/CompPack documentation built on May 7, 2019, 10:16 a.m.