dynr.mi | R Documentation |

Multiple Imputation of dynrModel objects

dynr.mi(dynrModel, which.aux = NULL, which.lag = NULL, lag = 0, which.lead = NULL, lead = 0, m = 5, iter = 5, imp.obs = FALSE, imp.exo = TRUE, diag = TRUE, Rhat = 1.1, conf.level = 0.95, verbose = TRUE, seed = NA)

`dynrModel` |
dynrModel object. data and model setup |

`which.aux` |
character. names of the auxiliary variables used in the imputation model |

`which.lag` |
character. names of the variables to create lagged responses for imputation purposes |

`lag` |
integer. number of lags of variables in the imputation model |

`which.lead` |
character. names of the variables to create leading responses for imputation purposes |

`lead` |
integer. number of leads of variables in the imputation model |

`m` |
integer. number of multiple imputations |

`iter` |
integer. number of MCMC iterations in each imputation |

`imp.obs` |
logical. flag to impute the observed dependent variables |

`imp.exo` |
logical. flag to impute the exogenous variables |

`diag` |
logical. flag to use convergence diagnostics |

`Rhat` |
numeric. value of the Rhat statistic used as the criterion in convergence diagnostics |

`conf.level` |
numeric. confidence level used to generate confidence intervals |

`verbose` |
logical. flag to print the intermediate output during the estimation process |

`seed` |
integer. random number seed to be used in the MI procedure |

See the demo, `demo(package='dynr', 'MILinearDiscrete')`

, for an illustrative example
of using `dynr.mi`

to implement multiple imputation with
a vector autoregressive model.

an object of ‘dynrMi’ class that is a list containing: 1. the imputation information, including a data set containing structured lagged and leading variables and a ‘mids’ object from mice() function; 2. the diagnostic information, including trace plots, an Rhat plot and a matrix containing Rhat values; 3. the estimation results, including parameter estimates, standard error estimates and confidence intervals.

Ji, L., Chow, S-M., Schermerhorn, A.C., Jacobson, N.C., & Cummings, E.M. (2018). Handling Missing Data in the Modeling of Intensive Longitudinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 1-22.

Yanling Li, Linying Ji, Zita Oravecz, Timothy R. Brick, Michael D. Hunter, and Sy-Miin Chow. (2019). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 13, 302-311.

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