Description Usage Arguments Value Author(s) Examples
View source: R/emAllFeatures.R
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 2 | emAllFeatures(known, unknown, p = rep(0.2, 5), maxiter = 2000,
conv = 1e-05)
|
known |
A data set with class as the first column. This data set has known class proportions. |
unknown |
A data set whose proportions will be estimated. This data set may have class as the first column, to return an MSE to compare the estimates to the actual proportions. |
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 estimated mixing proportions for the unknown data set.
iter = number of iterations before EM 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.
mse = MSE of actual unknown class proportions (if in data set) versus estimated.
Jennifer Starling
1 2 | ## Define ctrl object.
em <- emAllFeatures(df1,df2,p=rep(.2,5),maxiter=1000,conv=.00001)
|
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