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

In dynr: Dynamic Models with Regime-Switching

Cook a dynr model to estimate its free parameters

Description

Cook a dynr model to estimate its free parameters

Usage

dynr.cook(dynrModel, conf.level = 0.95, infile, optimization_flag = TRUE,
  hessian_flag = TRUE, verbose = TRUE, weight_flag = FALSE,
  debug_flag = FALSE, perturb_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
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
perturb_flag	a flag (TRUE/FLASE) indicating whether to perturb the latent states during estimation. Only useful for ensemble forecasting.

Details

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. Note that if theta.formula is provided in dynrModel@dynamics, this assumes that random effects are present in the dynamic equation. This would call an internal function to insert the random effect components as additional state variables. In this case, the last set of elements (rows) in eta_smooth_final would contain the estimated random effect components.

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.

The exit flag given after optimization has finished is from the SLSQP optimizer. Generally, error codes have negative values and successful codes have positive values. However, codes 5 and 6 do not indicate the model converged, but rather simply ran out of iterations or time, respectively. A more full description of each code is available at https://nlopt.readthedocs.io/en/latest/NLopt_Reference/#return-values and is also listed in the table below.

NLOPT Term	Numeric Code	Description
SUCCESS	1	Generic success return value.
STOPVAL_REACHED	2	Optimization stopped because stopval (above) was reached.
FTOL_REACHED	3	Optimization stopped because ftol_rel or ftol_abs (above) was reached.
XTOL_REACHED	4	Optimization stopped because xtol_rel or xtol_abs (above) was reached.
MAXEVAL_REACHED	5	Optimization stopped because maxeval (above) was reached.
MAXTIME_REACHED	6	Optimization stopped because maxtime (above) was reached.
FAILURE	-1	Generic failure code.
INVALID_ARGS	-2	Invalid arguments (e.g. lower bounds are bigger than upper bounds, an unknown algorithm was specified, etcetera).
OUT_OF_MEMORY	-3	Ran out of memory.
ROUNDOFF_LIMITED	-4	Halted because roundoff errors limited progress. (In this case, the optimization still typically returns a useful result.)
FORCED_STOP	-5	Halted because of a forced termination: the user called nlopt_force_stop(opt) on the optimization's nlopt_opt object opt from the user's objective function or constraints.
NONFINITE_FIT	-6	Fit function is not finite (i.e., is NA, NaN, Inf or -Inf).

The last row of this table corresponding to an exit code of -6, is not from NLOPT, but rather is specific to the dynr package.

Value

Object of class dynrCook.

See Also

autoplot, coef, confint, deviance, initialize, logLik, names, nobs, plot, print, show, summary, vcov.

Examples

# Minimal model
require(dynr)

meas <- prep.measurement(
	values.load=matrix(c(1, 0), 1, 2),
	params.load=matrix(c('fixed', 'fixed'), 1, 2),
	state.names=c("Position","Velocity"),
	obs.names=c("y1"))

ecov <- prep.noise(
	values.latent=diag(c(0, 1), 2),
	params.latent=diag(c('fixed', 'dnoise'), 2),
	values.observed=diag(1.5, 1),
	params.observed=diag('mnoise', 1))

initial <- prep.initial(
	values.inistate=c(0, 1),
	params.inistate=c('inipos', 'fixed'),
	values.inicov=diag(1, 2),
	params.inicov=diag('fixed', 2))

dynamics <- prep.matrixDynamics(
	values.dyn=matrix(c(0, -0.1, 1, -0.2), 2, 2),
	params.dyn=matrix(c('fixed', 'spring', 'fixed', 'friction'), 2, 2),
	isContinuousTime=TRUE)

data(Oscillator)
data <- dynr.data(Oscillator, id="id", time="times", observed="y1")

model <- dynr.model(dynamics=dynamics, measurement=meas,
	noise=ecov, initial=initial, data=data)

## Not run: 
# Now cook the model!
cook <- dynr.cook(model,
	verbose=FALSE, optimization_flag=FALSE, hessian_flag=FALSE)

## End(Not run)

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