generator_g: Build generator matrices of a continuous-time Markov chain

In LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

Build generator matrices of a continuous-time Markov chain

Description

This function builds infinitesimal generator matrices for a continuous-time Markov chain based on a design matrix and coefficient matrix.

Usage

generator_g(Z, beta, Eta = NULL, byrow = FALSE, report = TRUE)

Arguments

Z	Covariate design matrix with or without intercept column, i.e. of dimension c(nObs, p) or c(nObs, p+1). If not provided, intercept column is added automatically.
beta	Matrix of coefficients for the off-diagonal elements of the generator matrix of dimension c(nStates * (nStates-1), p+1). First columns contains the intercepts.
Eta	optional pre-calculated matrix of linear predictors of dimension c(nObs, nStates * (nStates-1)). If provided, no Z and beta are necessary and will be ignored.
byrow	logical indicating if the generator matrices should be filled by row
report	logical, indicating whether the generator matrices Q should be reported from the fitted model. Defaults to TRUE, but only works if when automatic differentiation with RTMB is used.

Details

Off-diagonal entries are calculated as \exp(Z \beta_i) to ensure positivity. The diagonal entries are then set to the negative row sums, which is required for generator matrices.

Value

array of infinitesimal generator matrices of dimension c(nStates, nStates, nObs)

See Also

Other transition probability matrix functions: generator(), tpm(), tpm_ct(), tpm_emb(), tpm_emb_g(), tpm_g(), tpm_g2(), tpm_p()

Examples

# 2 states: 2 free off-diagonal elements
generator(rep(-1, 2))
# 3 states: 6 free off-diagonal elements
generator(rep(-2, 6))

LaMa documentation built on July 25, 2026, 1:08 a.m.