Ysimulation: Simulation of the vector of responses Y. Data preparation...

In MMLR: Fitting Markov-Modulated Linear Regression Models

Description

Additional function to be used for simulation purposes (academical or research). Simulating the vector of responses Y according to the formula (see details).

Usage

Ysimulation(t, I, X, lambda, sigma = 1, beta)

Arguments

t	Vector of the observed time (n x 1), n – number of observations
I	Vector of the initial states (n x 1), n – number of observations
X	Matrix of predictors (n x k), n - number of observations, k - number of columns (k - 1 - number of regressors).
lambda	Matrix with the known transition rates λ_{i,j}, (m x m), m – number of states
sigma	Scalar, the standard deviation of the disturbance term
beta	Matrix (k x m), k - number of columns (k - 1 - number of regressors), m - number of states, m = 2,3,.. .

Details

The i-th response $Y_i$ is defined by the following formula: $Y_i(t)=x_iβ + Z_i sqrtt, i=1,...,n.$ The vector with stationary probabilities is user-defined vector.

Value

Vector with new response values of vector Y (n x 1)

Examples

Xtest <- cbind(rep_len(1,10),c(2,5,7,3,1,1,2,2,3,6), c(5,4,1,2,3,2,3,5,2,2))
tGiven <- matrix (c(0.9,1.18,1,1.6,1.4,1.7,1.9,1.45,1.5,2.14), nrow = 10, ncol = 1)
initState <- matrix (c(2,1,1,2,2,2,1,1,2,1),nrow = 10, ncol = 1)
lambda <- matrix(c(0, 0.33, 0.45, 0), nrow = 2, ncol = 2, byrow = TRUE)
beta <- matrix(c(1, 2, 3, 4, 6, 8), nrow = 3, ncol = 2, byrow = TRUE)
Ysimulation(tGiven,initState,Xtest,lambda,1,beta)

MMLR documentation built on Jan. 9, 2020, 5:06 p.m.