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Build the transition probability matrix from unconstrained parameter vector

tpm.Rd

Markov chains are parametrised in terms of a transition probability matrix \(\Gamma\), for which each row contains a conditional probability distribution of the next state given the current state. Hence, each row has entries between 0 and 1 that need to sum to one.

For numerical optimisation, we parameterise in terms of unconstrained parameters, thus this function computes said matrix from an unconstrained parameter vector via the inverse multinomial logistic link (also known as softmax) applied to each row.

Usage

tpm(
  beta,
  Z = NULL,
  Eta = NULL,
  byrow = FALSE,
  ref = NULL,
  ad = NULL,
  report = TRUE,
  param = NULL
)

Arguments

beta

parameters; either

  • a vector of length nStates * (nStates-1), or

  • a matrix of dimension c(nStates * (nStates-1), p+1) if design matrix Z is also provided.

Z

optional covariate design matrix with or without intercept column, i.e. of dimension c(nObs, p) or c(nObs, p+1). If provided, beta needs to be a matrix of dimension c(nStates * (nStates-1), p+1).

Eta

optional pre-calculated matrix of linear predictors of dimension c(nObs, nStates * (nStates-1)). If provided, Z and beta will be ignored.

byrow

logical indicating if each transition probability matrix should be filled by row. Defaults to FALSE, but should be set to TRUE if one wants to work with a matrix of beta parameters returned by popular HMM packages like moveHMM, momentuHMM, or hmmTMB.

ref

optional integer vector of length nStates giving, for each row, the column index of the reference state (its predictor is fixed to 0). Defaults to the diagonal (ref = 1:nStates).

ad

logical; whether to use automatic differentiation. Determined automatically — for debugging only.

report

logical; if TRUE (default), delta, Gamma, allprobs, and trackID are reported from the fitted model. Requires ad = TRUE.

param

depricated, please use argument beta instead.

Value

Transition probability matrix of dimension c(nStates, nStates) or array of such matrices of dimension c(nStates, nStates, nObs) if Z or Eta is provided.

See also

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

Examples

## homogeneous Markov chain
# 2 states: 2 = 2*(2-1) free off-diagonal elements
par <- rep(-2, 2)
Gamma <- tpm(par)
# 3 states: 6 = 3*(3-1) free off-diagonal elements
par <- rep(-3, 6)
Gamma <- tpm(par)
# 4 states: 12 = 4*(4-1) free off-diagonal elements
par <- rep(-4, 12)
Gamma <- tpm(par)

## inhomogeneous Markov chain
# t.p.m. depends on covariates
z1 <- runif(100); z2 <- runif(100) # 2 covariates
Z <- cbind(1, z1, z2) # design matrix
beta0 <- c(-2, -2); beta1 = c(1, -2); beta2 = c(2, -1) # coefficients for intercept and covariates
beta <- cbind(beta0, beta1, beta2) # coefficient matrix; with intercepts!
Gamma <- tpm(beta, Z) # array with 100 slices