raddreg: Random data generation from the Gaussian additive...

In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

Random data generation from the Gaussian additive (Markov-switching) model for hhsmm model

Description

Generates vectors of covariate and response observations from the Gaussian additive (Markov-switching) model, using B-Splines in a specified state and using the parameters of a specified model

Usage

raddreg(j, model, covar, ...)

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

Value

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

Author(s)

Morteza Amini, [e-mail]

References

Langrock, R., Adam, T., Leos-Barajas, V., Mews, S., Miller, D. L., and Papastamatiou, Y. P. (2018). Spline-based nonparametric inference in general state-switching models. Statistica Neerlandica, 72(3), 179-200.

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(-21, -83, 33),
coef = list(array(c(1, 8, 52, 27, 38), dim = c(5, 1, 1)),  
array(c(99, 87, 94, 77, 50), dim = c(5, 1, 1)), 
array(c(-1, -8, -40, -22, -28), dim = c(5, 1, 1))),
sigma = list(0.2, 0.4, 0.1))
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dnorm_additive_reg, semi = semi)
train <- simulate(model, nsim = 70, seed = 1234, 
remission = raddreg, covar = list(mean = 0, cov = 1))
plot(train$x[, 1] ~ train$x[, 2], col = train$s, pch = 16, 
xlab = "x", ylab = "y")

hhsmm documentation built on Aug. 8, 2023, 9:06 a.m.