This function reorders the estimated states, which can be useful for a comparison to true parameters or the interpretation of states.

## Usage

reorder_states(x, state_order)

## Arguments

x

An object of class fHMM_model.

state_order

A vector or a matrix which determines the new ordering.

• If x$data$controls$hierarchy = FALSE, state_order must be a vector of length x$data$controls$states with integer values from 1 to x$data$controls$states. If the old state number x should be the new state number y, put the value x at the position y of state_order. E.g. for a 2-state HMM, specifying state_order = c(2,1) swaps the states. • If x$data$controls$hierarchy = TRUE, state_order must be a matrix of dimension x$data$controls$states[1] x x$data$controls$states[2] + 1. The first column orders the coarse-scale states with the logic as described above. For each row, the elements from second to last position order the fine-scale states of the coarse-scale state specified by the first element. E.g. for an HHMM with 2 coarse-scale and 2 fine-scale states, specifying state_order = matrix(c(2,1,2,1,1,2),2,3) swaps the coarse-scale states and the fine-scale states of coarse-scale state 2.

## Value

An object of class fHMM_model, in which states are reordered.

## Examples

data("dax_model_3t")
reorder_states(dax_model_3t, state_order = 3:1)
#> fHMM fitted model:
#> * total estimation time: 7 mins
#> * accepted runs: 21 of 100
#> * log-likelihood: 16913.33