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

## 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.

## 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
```