RJaCGH (version 1.1.1)

simulateRJaCGH: 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

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

Arguments

n

Number of observations to simulate

x

Distance to the next observation. Must be a vector of size n-1. If NULL, a normal sample with 0 and 1 parameters 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.

q

q parameter of the transition matrix. Must be a square matrix with the same size as the number of hidden states. By default, is -beta

Value

• A list with components
• states
  Sequence of hidden states
• y
  Observations

Details

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

References

Oscar M. Rueda and Ramon Diaz Uriarte. A flexible, accurate and extensible statistical method for detecting genomic copy-number changes. http://biostats.bepress.com/cobra/ps/art9/. {http://biostats.bepress.com/cobra/ps/art9/}.

See Also

Q.NH, RJaCGH

Examples

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)