tpm_g {LaMa} | R Documentation |
Build all transition probability matrices of an inhomogeneous HMM
Description
In an HMM, we often model the influence of covariates on the state process by linking them to the transition probabiltiy matrix. Most commonly, this is done by specifying a linear predictor
\eta_{ij}^{(t)} = \beta^{(ij)}_0 + \beta^{(ij)}_1 z_{t1} + \dots + \beta^{(ij)}_p z_{tp}
for each off-diagonal element (i \neq j
) of the transition probability matrix and then applying the inverse multinomial logistic link (also known as softmax) to each row.
This function efficiently calculates all transition probabilty matrices for a given design matrix Z
and parameter matrix beta
.
Usage
tpm_g(Z, beta, byrow = FALSE, ad = NULL, report = TRUE)
Arguments
Z |
covariate design matrix with or without intercept column, i.e. of dimension c(n, p) or c(n, p+1) If |
beta |
matrix of coefficients for the off-diagonal elements of the transition probability matrix Needs to be of dimension c(N*(N-1), p+1), where the first column contains the intercepts. |
byrow |
logical indicating if each transition probability matrix should be filled by row Defaults to |
ad |
optional logical, indicating whether automatic differentiation with |
report |
logical, indicating whether the coefficient matrix |
Value
array of transition probability matrices of dimension c(N,N,n)
See Also
Other transition probability matrix functions:
generator()
,
tpm()
,
tpm_cont()
,
tpm_emb()
,
tpm_emb_g()
,
tpm_p()
Examples
Z = matrix(runif(200), ncol = 2)
beta = matrix(c(-1, 1, 2, -2, 1, -2), nrow = 2, byrow = TRUE)
Gamma = tpm_g(Z, beta)