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.