mritc (version 0.3-2)

emnormmix: Estimate the Parameters of a Normal Mixture Model Using the EM Algorithm

Description

Use the EM Algorithm to estimate the parameters of a normal mixture model.

Usage

emnormmix(y, prop, mu, sigma, err, maxit, verbose)

Arguments

y

vector of observations.

prop

vector of initial estimate of the proportions of different components of a normal mixture model.

mu

vector of initial estimate of the means of different components of a normal mixture model.

sigma

vector of initial estimate of the standard deviations of different components of a normal mixture model.

err

relative maximum error(s) used to decide when to stop the iteration. It could be a vector of length three corresponding to the relative maximum errors of the means, standard deviations, and proportions of all components of a normal mixture

maxit

maximum number of iterations to perform.

verbose

logical. If TRUE, then indicate the level of output as the algorithm runs after every 10 iterations.

Value

• prop
  a vector of estimated proportions of different components of a normal mixture model.
• mu
  a vector of estimated means of different components of a normal mixture model.
• sigma
  a vector of estimated standard deviations of different components of a normal mixture model.

Details

It is tailor-made for the case when observations are from a finite set (MRI data for example). The table lookup method is used to speed up the computation.

Examples

prop <- c(0.3, 0.3, 0.4)
  mu <- c(-10, 0, 10)
  sigma <- rep(1, 3)
  y<- floor(rnormmix(n=100000, prop, mu, sigma)[,1])
  initial <- initOtsu(y, 2)
  emnormmix(y=y, prop=initial$prop, mu=initial$mu, sigma=initial$sigma,
            err=1e-7, maxit=100, verbose=TRUE)