EM: EM Algorithm for Fitting the Hidden Markov Model
In Tieming/A-Bayesian-hidden-Markov-model-for-detecting-DMRs: DMR detection by HMM
Description
Usage
Arguments
Value
Description
This function implements the EM algorithm to estimate HMM parameters,
and find the best sequence of hidden states based on model fitting.
Usage
1
EM(initial.value, obs)
Arguments
initial.value
Output from initial.value function.
obs
Output from read.process function.
Value
res A matrix contains tansformed observations for each bin and the predicted direction for each bin (0-normal, 1-hyper, 2-hypo).
para Fitted parameters for HMM.
Tieming/A-Bayesian-hidden-Markov-model-for-detecting-DMRs documentation built on May 29, 2019, 12:36 p.m.
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Description Usage Arguments Value
Description
This function implements the EM algorithm to estimate HMM parameters, and find the best sequence of hidden states based on model fitting.
Usage
1 | EM(initial.value, obs)
|
Arguments
initial.value |
Output from initial.value function. |
obs |
Output from read.process function. |
Value
res A matrix contains tansformed observations for each bin and the predicted direction for each bin (0-normal, 1-hyper, 2-hypo).
para Fitted parameters for HMM.
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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