RJaCGH assumes a non-homogeneous transition matrix with this form:
Q[i,j] = exp(-beta[i,j] + beta[i,j]*x) / sum(i,.) {exp(-beta[i,.] +
beta[i,.]*x}
All beta[i,i] are constrained to be zero, for the model to be
identifiable.
All beta[i,j] are positive. This model is chosen for its simplicity
and because it agrees with
biological assumptions, for it makes the
probabilities of staying in the same state decreasing with the
distance to the next gene, and reaches a probability of 1/number of
hidden state when the distance to the next gene is the maximum. To avoid overflow errors, RJaCGH normalizes distances to be between 0
and 1.
~Describe the value returned
A matrix with the transition probabilities for that distance.
Rueda OM, Diaz-Uriarte R.
Flexible and Accurate Detection of Genomic Copy-Number Changes from
aCGH.
PLoS Comput Biol. 2007;3(6):e122
[object Object],[object Object]
plot.Q.NH
## Model with two hidden states
## Note that RJaCGH normalizes distances to be between 0 and 1
beta <- matrix(c(0, 1, 3, 0), 2, 2)
Q.NH(beta=beta, x=0.4)
models