dynr.cook: Cook a dynr model to estimate its free parameters

Description Usage Arguments Details Examples

Description

Cook a dynr model to estimate its free parameters

Usage

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dynr.cook(dynrModel, conf.level = 0.95, infile, verbose = TRUE,
  weight_flag = FALSE, debug_flag = FALSE)

Arguments

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

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

Details

Free parameter estimation uses the SLSQP routine from NLOPT.

The typical items returned in the cooked model are the smoothed latent variable estimates only. The time-varying latent variable means are called eta_smooth_final; the time-varying latent variable (co-)variances are called error_cov_smooth_final; and the time-varying smoothed probability of each regime is called pr_t_given_T.

When debug_flag is TRUE, then additional information is passed into the cooked model. This information can get quite large, so it is not returned unless requested. The information gets large because these items often depend on the regime in addition to time. The updated latent states for each possible regime are in eta_regime_t; the updated latent covariances for each possible regime are in error_cov_regime_t; the latent residual (innovation vector) from each regime to each regime is stored in innov_vec; and the inverse of the updated latent covariance matrix from each regime to each regime is in inverse_residual_cov.

Examples

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#fitted.model <- dynr.cook(model)


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