em.twocomp.m2: em.twocomp.m2

In bimixt: Estimates Mixture Models for Case-Control Data

Description

Expectation maximization (EM) algorithm for estimating two-component Gaussian mixture models. This is used as an internal method and is called twice from bc.fourcomp: once for the cases and once for the controls (four component model).

Usage

em.twocomp.m2(x.all, max.iters = 1000, errtol = 1e-09, start.vals=NULL)

Arguments

x.all	vector of data
max.iters	the maximum number of iterations to run
errtol	Error tolerance level. Approximates convergence of the maximum log likelihood value.
start.vals	starting values for the EM algorithm. If NA, the starting values are estimated from the data.

Value

max.loglike	the maximum log likelihood value for the algorithm
mu	estimated means for each component
sig	estimated standard deviation for each component
pi	estimated proportion of data in each component
n.iters	the number of iterations the algorithm took to converge

Author(s)

Michelle Winerip, Garrick Wallstrom, Joshua LaBaer

References

Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin. "Maximum likelihood from incomplete data via the EM algorithm." Journal of the royal statistical society. Series B (methodological) (1977): 1-38.

See Also

bc.binorm bc.twocomp bc.fourcomp em.twocomp.m1 em.twocomp.m3

bimixt documentation built on May 2, 2019, 3:31 p.m.