Simulate observations form a hidden Markov model with non-homogeneous transition probabilities.

Description

This function simulates observations from a hidden Markov model with normal distributed observations and non-homogeneous transition matrix.

Usage

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simulateRJaCGH(n, x = NULL, mu, sigma.2, beta, start)

Arguments

n

Number of observations to simulate

x

Distance to the next observation. Must be a vector of size n-1 and normalized between zero and one. If NULL, a vector of zeros is taken

mu

Vector of means for the hidden states

sigma.2

Vector of variances for the hidden states

beta

beta parameter of the transition matrix. Must be a square matrix with the same size as the number of hidden states.

start

Starting states of the sequence. Must be an integer from 1 to the number of hidden states.

Details

Please note that in RJaCGH model, parameter q is taken as -beta

Value

A list with components

states

Sequence of hidden states

y

Observations

Author(s)

Oscar M. Rueda and Ramon Diaz-Uriarte

References

Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122

See Also

Q.NH, RJaCGH

Examples

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beta <- matrix(c(0, 5, 1, 1,  0, 1, 3, 5, 0), 3)
obs <- simulateRJaCGH(n=200, x=rexp(199), mu=c(-3, 0, 3), sigma.2=c(1,1,1),
beta=beta, start=2)
plot(obs$y, col=obs$states)