em.twocomp.m3: em.twocomp.m3

Description

Expectation maximization (EM) algorithm for estimating two-component Gaussian mixtures with different mixture proportions for cases and controls (two component unconstrained model). This is used as an internal method and is called from bc.twocomp.

Usage

em.twocomp.m3(x.all, case.indicator, max.iters = 1000, errtol = 1e-09, 
control.comp = 1, start.vals=NULL)

Arguments

x.all

vector of cases and controls

case.indicator

a vector of equal length to x.all with 1's in the case positions and 0's in the control positions

max.iters

the maximum number of iterations to run

errtol

Error tolerance level. Approximates convergence of the maximum log likelihood value.

control.comp

indicator of which component contains the controls (1 or 2)

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 deviations for each component
pi.cs: estimated proportion of cases in each component
pi.ctrl: estimated proportion of controls in each component
n.iters: the number of iterations the algorithm took to converge
control.comp: indicator of which component contains the controls (1 or 2)

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.

Description

Usage

Arguments

Value

References

See Also