dynr.mi: Multiple Imputation of dynrModel objects

In dynr: Dynamic Models with Regime-Switching

Multiple Imputation of dynrModel objects

Description

Multiple Imputation of dynrModel objects

Usage

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)

Arguments

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

Details

See the demo, demo(package='dynr', 'MILinearDiscrete'), for an illustrative example of using dynr.mi to implement multiple imputation with a vector autoregressive model.

Value

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.

References

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.

dynr documentation built on May 29, 2024, 2:49 a.m.