hhsmm (version 0.4.2)

rmixlm: Random data generation from the mixture of Gaussian linear (Markov-switching) models for hhsmm model

Description

Generates vectors of covariate and response observations from mixture of Gaussian linear (Markov-switching) models in a specified state and using the parameters of a specified model

Usage

rmixlm(j, model, covar, ...)

Value

a random matrix of observations from mixture of Gaussian linear (Markov-switching) models, in which the first columns are associated with the responses and the last columns are associated with the covariates

Arguments

j

a specified state

model

a hhsmmspec model

covar

either a function which generates the covariate vector or a list containing the following items:

• mean the mean vector of covariates (to be generated from multivariate normal distribution)

• cov the variance-covariance matrix of covariates (to be generated from multivariate normal distribution)

...

additional arguments of the covar function

Author

Morteza Amini,

References

Kim, C. J., Piger, J. and Startz, R. (2008). Estimation of Markov regime-switching regression models with endogenous switching. Journal of Econometrics, 143(2), 263-273.

Examples

J <- 3
initial <- c(1, 0, 0)
semi <- rep(FALSE, 3)
P <- matrix(c(0.5, 0.2, 0.3, 0.2, 0.5, 0.3, 0.1, 0.4, 0.5), nrow = J, 
byrow = TRUE)
par <- list(intercept = list(3, list(-10, -1), 14),
coefficient = list(-1, list(1, 5), -7),
csigma = list(1.2, list(2.3, 3.4), 1.1),
mix.p = list(1, c(0.4, 0.6), 1))
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dmixlm, semi = semi)

#use the covar as the list of mean and 
#variance of the normal distribution 

train1 <- simulate(model, nsim = c(20, 30, 42, 50), seed = 1234, 
remission = rmixlm, covar = list(mean = 0, cov = 1))
plot(train1$x[,1] ~ train1$x[,2], col = train1$s, pch = 16, 
xlab = "x", ylab = "y")

#use the covar as the runif function 
#to generate one covariate from standard uniform distribution 

train2 <- simulate(model, nsim = c(20, 30, 42, 50), seed = 1234, 
remission = rmixlm, covar = runif, 1)
plot(train2$x[,1] ~ train2$x[,2], col = train2$s, pch = 16, 
xlab = "x", ylab = "y")