simulateRJaCGH: Simulate observations form a hidden Markov model with...
In RJaCGH: Reversible Jump MCMC for the Analysis of CGH Arrays

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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.

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

A list with components

`states`	Sequence of hidden states
`y`	Observations

Oscar M. Rueda and Ramon Diaz-Uriarte

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

Q.NH, RJaCGH

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)