Description Usage Arguments Value Author(s) References See Also
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).
1 | em.twocomp.m2(x.all, max.iters = 1000, errtol = 1e-09, start.vals=NULL)
|
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 |
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 |
Michelle Winerip, Garrick Wallstrom, Joshua LaBaer
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.
bc.binorm
bc.twocomp
bc.fourcomp
em.twocomp.m1
em.twocomp.m3
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