kDGLM (version 1.2.12)

simulate.fitted_dlm: Draw samples from the distribution of the latent states

Description

This is function draws samples from the latent states using the backward sampling algorithm. See WestHarr-DLM;textualkDGLM, chapter 15, for details.

Usage

# S3 method for fitted_dlm
simulate(object, nsim, seed = NULL, lag = -1, ...)

Value

A list containing the following values:

• theta array: An array containing a sample of the latent states. Dimensions are n x t x nsim, where n is the number of latent states in the model and t is the number of observed values.

• lambda array: An array containing a sample of the linear predictors. Dimensions are k x t x nsim, where k is the number of linear predictors in the model and t is the number of observed values.

• param list: A named list containing, for each model outcome, an array with the samples of the parameters of the observational model. Each array will have dimensions l x t x nsim, where l is the number of parameters in the observational model and t is the number of observed values.

Arguments

object

fitted_dlm: A fitted model from which to sample.

nsim

integer: The number of samples to draw.

seed

integer: An object specifying if and how the random number generator should be initialized.

lag

integer: The relative offset for forecast. Values for time t will be calculated based on the filtered values of time t-h. If lag is negative, then the smoothed distribution for the latent states will be used.

...

Extra arguments passed to the plot method.

See Also

Other auxiliary functions for fitted_dlm objects: coef.fitted_dlm(), eval_dlm_norm_const(), fit_model(), forecast.fitted_dlm(), kdglm(), smoothing(), update.fitted_dlm()

Examples

structure <- polynomial_block(mu = 1, D = 0.95) +
  polynomial_block(V = 1, D = 0.95)

outcome <- Normal(mu = "mu", V = "V", data = cornWheat$corn.log.return[1:500])
fitted.data <- fit_model(structure, corn = outcome)

sample <- simulate(fitted.data, 5000)