Description Usage Arguments Details Value Author(s) Examples
The function hybrid.em.pav
executes the hybrid expectation-maximization pool-adjacent-violators algorithm given in the paper "Misclassified Group Tested Current Status Data." The function expectation
computes the "E" step in the EM-PAV hybrid algorithm. The iterations are done by the hybrid.em.pav
function.
1 2 | hybrid.em.pav(data, initial.p, threshold=0.01, alpha=1, beta=1)
expectation(p, delta, alpha=1, beta=1)
|
data |
This is a dataset as generated by the function |
initial.p |
These are the initial values for the algorithm for each individual in the dataset. Can come from an external source, or should indicate which column of |
threshold |
Point at which algorithm should stop. Is the sum of the squared distances between last iteration and current iteration. Default is 0.01. |
p |
Probabilities to be updated by the |
delta |
A scalar indicating the group test result. |
alpha |
Sensitivity. Default is 1. |
beta |
Specificity. Default is 1. |
This is an implementation of the EM algorithm for misclassified grouped current status data, using the pool-adjacent-violators algorithm (Ayers et al., 1955) as the maximization step.
results |
These should be treated as the right-continuous step function values (like the |
num.iterations |
The total number of iterations until convergence. |
diff |
The sum of the squared differences between the final iteration and the previous iteration. This is always less than the specified threshold. |
Lucia Petito
1 2 3 4 5 | data <- gen.data.weibull.unif(100, 2, 4, 25, 25, 1, 1)
out <- hybrid.em.pav(data, data$initial.p, alpha=0.9, beta=0.9)
head(out$results)
out$num.iterations
out$diff
|
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