Description Usage Arguments Details See Also Examples

Cook a dynr model to estimate its free parameters

1 2 3 |

`dynrModel` |
a dynr model compiled using dynr.model, consisting of recipes for submodels, starting values, parameter names, and C code for each submodel |

`conf.level` |
a cumulative proportion indicating the level of desired confidence intervals for the final parameter estimates (default is .95) |

`infile` |
(not required for models specified through the recipe functions) the name of a file that has the C codes for all dynr submodels for those interested in specifying a model directly in C |

`optimization_flag` |
a flag (TRUE/FALSE) indicating whether optimization is to be done. |

`hessian_flag` |
a flag (TRUE/FALSE) indicating whether the Hessian matrix is to be calculated. |

`verbose` |
a flag (TRUE/FALSE) indicating whether more detailed intermediate output during the estimation process should be printed |

`weight_flag` |
a flag (TRUE/FALSE) indicating whether the negative log likelihood function should be weighted by the length of the time series for each individual |

`debug_flag` |
a flag (TRUE/FALSE) indicating whether users want additional dynr output that can be used for diagnostic purposes |

Free parameter estimation uses the SLSQP routine from NLOPT.

The typical items returned in the cooked model are the filtered and smoothed latent variable estimates.
`eta_smooth_final`

, `error_cov_smooth_final`

and `pr_t_given_T`

are respectively
time-varying smoothed latent variable mean estimates, smoothed error covariance estimates,
and smoothed regime probability.
`eta_filtered`

, `error_cov_filtered`

and `pr_t_given_t`

are respectively
time-varying filtered latent variable mean estimates, filtered error covariance matrix estimates,
and filtered regime probability.

When `debug_flag`

is TRUE, then additional information is passed into the cooked model.
`eta_predicted`

, `error_cov_predicted`

, `innov_vec`

, and `residual_cov`

are respectively
time-varying predicted latent variable mean estimates, predicted error covariance matrix estimates, the error/residual estimates (innovation vector),
and the error/residual covariance matrix estimates.

`autoplot`

, `coef`

, `confint`

,
`deviance`

, `initialize`

, `logLik`

,
`names`

, `nobs`

, `plot`

, `print`

,
`show`

, `summary`

, `vcov`

.

1 | ```
#fitted.model <- dynr.cook(model)
``` |

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