# Methods for creating depmix transition and initial probability models

### Description

Create `transInit`

objects for `depmix`

models using
formulae and family objects.

### Usage

1 2 3 4 5 6 |

### Arguments

`formula` |
A model |

`data` |
An optional data.frame to interpret the variables from the formula argument in. |

`family` |
A family object; see details. |

`pstart` |
Starting values for the coefficients. |

`fixed` |
Logical vector indicating which paramters are to be fixed. |

`prob` |
Logical indicating whether the starting values for multinomial() family models are probabilities or logistic parameters (see details). |

`nstates` |
The number of states of the model. |

`object` |
An object of class |

`...` |
Not used currently. |

### Details

The `transInit`

model provides functionality for the multinomial
probabilities of the transitions between states, as well as for the
prior or initial probabilities. These probabilities may depend on
(time-varying) covariates. The model can be used with link functions
`mlogit`

and `identity`

; the latter is the default when no
covariates are. With the `mlogit`

link function, the transition
probabilities are modeled as baseline logistic multinomials (see
Agresti, 2002, p. 272 ff.).

Start values for the parameters may be provided using the `pstart`

argument; these can be provided as probabilities, the default option,
or as baseline logistic parameters, use the `prob`

argument to
specify the chosen option. The default baseline category is set to 1,
which can be modified through calling, say, family=multinomial(base=2).

Note that the transInit model extends the `response-class`

,
but that it actually lacks a reponse, i.e. the y-slot is empty, at the
time of construction, as the transitions are not observed.

`getdf`

returns the number of free parameters of a transInit model.

### Value

`transInit`

return objects of class `transInit`

; this class
extends the `response-class`

.

### Author(s)

Ingmar Visser & Maarten Speekenbrink

### References

Agresti, A. (2002). *Categorical Data Analysis*. Wiley series in
probability and mathematical statistics. Wiley-Interscience, Hoboken,
NJ, 2 edition.