This is function draws samples from the latent states using the backward sampling algorithm. See West and Harrison (1997) , chapter 15, for details.
Usage
# S3 method for fitted_dlm
simulate(object, nsim, seed = NULL, lag = -1, ...)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.
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.
See also
Other auxiliary functions for fitted_dlm objects:
coef.fitted_dlm(),
eval_dlm_norm_const(),
fit_model(),
forecast.fitted_dlm(),
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)