bwfw.hmm {PLIS} | R Documentation |
backward and forward inferences
Description
When L>1, calculate values for backward, forward variables, probabilities of hidden states. A supporting function called by em.hmm.
Usage
bwfw.hmm(x, pii, A, pc, f0, f1)
Arguments
x |
the observed Z values |
pii |
(prob. of being 0, prob. of being 1), the initial state distribution |
A |
A=(a00 a01\\ a10 a11), transition matrix |
pc |
(c[1], ..., c[L])–the probability weights in the mixture for each component |
f0 |
(mu, sigma), the parameters for null distribution |
f1 |
(mu[1], sigma[1]\\...\\mu[L], sigma[L])–an L by 2 matrix, the parameter set for the non-null distribution |
Details
calculates values for backward, forward variables, probabilities of hidden states,
–the lfdr variables and etc.
–using the forward-backward procedure (Rabiner 89)
–based on a sequence of observations for a given hidden markov model M=(pii, A, f)
–see Sun and Cai (2009) for a detailed instruction on the coding of this algorithm
Value
alpha |
rescaled backward variables |
beta |
rescaled forward variables |
lfdr |
lfdr variables |
gamma |
probabilities of hidden states |
dgamma |
rescaled transition variables |
omega |
rescaled weight variables |
Author(s)
Wei Z, Sun W, Wang K and Hakonarson H
References
Multiple Testing in Genome-Wide Association Studies via Hidden Markov Models, Bioinformatics, 2009
Large-scale multiple testing under dependence, Sun W and Cai T (2009), JRSSB, 71, 393-424
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Rabiner L (1989), Procedings of the IEEE, 77, 257-286.