pairwise_interface {plotHMM}

Pairwise algorithm

Description

Efficient implementation of pairwise algorithm in C++ code, for N data and S states.

Usage

pairwise_interface(
  log_emission_mat, log_transition_mat, log_alpha_mat, log_beta_mat)

Arguments

log_emission_mat, log_alpha_mat, log_beta_mat

N x S numeric matrices of log likelihood.

log_transition_mat

S x S numeric matrix; log_transition_mat[i,j] is the log probability of going from state i to state j.

Value

S x S x N-1 numeric array of log likelihood.

Author(s)

Toby Dylan Hocking

Examples

##simulated data.
seg.mean.vec <- c(2, 0, -1, 0)
data.mean.vec <- rep(seg.mean.vec, each=10)
set.seed(1)
N.data <- length(data.mean.vec)
y.vec <- rnorm(N.data, data.mean.vec)
##model.
n.states <- 3
log.A.mat <- log(matrix(1/n.states, n.states, n.states))
state.mean.vec <- c(-1, 0, 1)*0.1
sd.param <- 1
log.emission.mat <- dnorm(
  y.vec,
  matrix(state.mean.vec, N.data, n.states, byrow=TRUE),
  sd.param,
  log=TRUE)
log.pi.vec <- log(rep(1/n.states, n.states))
f.list <- plotHMM::forward_interface(log.emission.mat, log.A.mat, log.pi.vec)
b.mat <- plotHMM::backward_interface(log.emission.mat, log.A.mat)
log.gamma.mat <- plotHMM::multiply_interface(f.list$log_alpha, b.mat)
prob.mat <- exp(log.gamma.mat)
plotHMM::pairwise_interface(log.emission.mat, log.A.mat, f.list$log_alpha, b.mat)