| hmm.sop | R Documentation | 
A Hidden Markov Model for the classification of states in a time series. 
Based on the transition probabilities and the so called emission probabilities
(p(class|x)) the ‘prior probablilities’ of states (classes) in time period t 
given all past information in time period t are calculated.
hmm.sop(sv, trans.matrix, prob.matrix)
sv | 
 state at time 0  | 
trans.matrix | 
 matrix of transition probabilities  | 
prob.matrix | 
 matrix of   | 
Returns the ‘prior probablilities’ of states.
Daniel Fischer, Reinald Oetsch
Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces. Dissertation, University of Dortmund. URL http://hdl.handle.net/2003/2789
calc.trans
library(MASS)
data(B3)
trans.matrix <- calc.trans(B3$PHASEN)
# Calculate posterior prob. for the classes via lda
prob.matrix <- predict(lda(PHASEN ~ ., data = B3))$post
errormatrix(B3$PHASEN, apply(prob.matrix, 1, which.max))
prior.prob <- hmm.sop("2", trans.matrix, prob.matrix)
errormatrix(B3$PHASEN, apply(prior.prob, 1, which.max))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.