dQ_em: Derivative function of the Q function of an EM algorithm
In ick003/SpTMixture: Mixture models with spatio-temporal dependencies
Description
Usage
Arguments
References
Examples
Description
Derivative function of the Q function for a mixture (EM)
Usage
1
Arguments
theta
parameters of the mixture that need estimation
y
observations
p.theta
result from the expectation step (EM - probabilities)
mean_func
function to calculate the mean for each mixture. The result is a matrix where the number of columns is the number of element in the mixture.
References
Dempster, A. and Laird, N. and Rubin, D. (1977) Maximum likelihood from incomplete data via the EM algorithm. JRSS B 39 1–38.
Examples
1
dQ_em()
ick003/SpTMixture documentation built on May 18, 2019, 2:32 a.m.
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Description Usage Arguments References Examples
Description
Derivative function of the Q function for a mixture (EM)
Usage
1 |
Arguments
theta |
parameters of the mixture that need estimation |
y |
observations |
p.theta |
result from the expectation step (EM - probabilities) |
mean_func |
function to calculate the mean for each mixture. The result is a matrix where the number of columns is the number of element in the mixture. |
References
Dempster, A. and Laird, N. and Rubin, D. (1977) Maximum likelihood from incomplete data via the EM algorithm. JRSS B 39 1–38.
Examples
1 | dQ_em()
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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