emCalcMix: emCalcMix(x,densities,p,maxiter,conv)
In jstarling1/varstar: What the package does (short line)

Description Usage Arguments Value Author(s) Examples

This function performs the Expectation-Maximization Algorithm to estimate a vector of class proportions for a single feature in a data set wih an unknown set of class proportions. Called by featureMixtureProportion.

1	emCalcMix(x, densities, p = rep(0.2, 5), maxiter = 2000, conv = 1e-05)

`x`	A vector of observed feature values for specified feature. Defaults to NULL.
`densities`	List of k density function; one per class. Flexible length, which must equal k.
`p`	Vector of class starting proportion estimates. Default is .2 for 5 classes.
`maxiter`	Maximum number of iterations of the EM algorithm allowed. Default is 2000.
`conv`	Convergence criteria for change in likelihood function. Change between

A list containing the following components:

p = vector of 5 estimated mixing proportions for feature.

iter = number of iterations before algorithm converged or was stopped.

allp = (iter x 5) data frame containing all phat iterated estimates.

logl = Vector of log-likelihood values, with one log-lik value for each iteration.

Jennifer Starling

## Define ctrl object.
em <- emCalcMix(f_sample,densities,p=rep(.2,5),maxiter=1000,conv=.00001)

##Sample density function:
f_class1 = df[df$class=="eb",f_name]
    	kd1  = density(f_class1)
    	dens1 = function(x)
	{
		min_den <- quantile(kd1$y,.05)
		de <- approx(kd1$x,kd1$y,x)$y
		de[is.na(de)] <- min_den
		return(de)
	}