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 |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Define ctrl object.
em <- emCalcMix(f_sample,densities,p=rep(.2,5),maxiter=1000,conv=.00001)
##Sample density function:
f_class1 = df[df$class=="eb",f_name]
<c2><a0> <c2><a0> kd1 <c2><a0>= density(f_class1)
<c2><a0> <c2><a0> dens1 = function(x)
{
min_den <- quantile(kd1$y,.05)
de <- approx(kd1$x,kd1$y,x)$y
de[is.na(de)] <- min_den
return(de)
}
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