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noise_block

Source: R/structure_helper.R
noise_block.Rd

Creates the structure for a Noise block. This block represents an independent random noise that should be added to the linear predictor. The variance of the noise cannot be formally estimated, as such we use a discount strategy similar to that of West and Harrison (1997) to specify it.

Usage

noise_block(..., name = "Noise", D = 0.99, R1 = 0.1, H = 0)

Arguments

...

Named values for the planning matrix.

name

String: An optional argument providing the name for this block. Can be useful to identify the models with meaningful labels, also, the name used will be used in some auxiliary functions.

D

scalar or vector: A sequence of values specifying the desired discount factor for each time. It should have length 1 or t, where t is the size of the series. If both D and H are specified, the value of D is ignored.

R1

scalar: The prior variance of the noise.

H

scalar: The variance of the noise. If both D and H are specified, the value of D is ignored.

Value

A dlm_block object containing the following values:

  • FF Array: A 3D-array containing the regression matrix for each time. Its dimension should be n x k x t, where n is the number of latent states, k is the number of linear predictors in the model and t is the time series length.

  • FF.labs Matrix: A n x k character matrix describing the type of value of each element of FF.

  • G Matrix: A 3D-array containing the evolution matrix for each time. Its dimension should be n x n x t, where n is the number of latent states and t is the time series length.

  • G.labs Matrix: A n x n character matrix describing the type of value of each element of G.

  • D Array: A 3D-array containing the discount factor matrix for each time. Its dimension should be n x n x t, where n is the number of latent states and t is the time series length.

  • H Array: A 3D-array containing the covariance matrix of the noise for each time. Its dimension should be the same as D.

  • a1 Vector: The prior mean for the latent vector.

  • R1 Matrix: The prior covariance matrix for the latent vector.

  • var.names list: A list containing the variables indexes by their name.

  • order Positive integer: Same as argument.

  • n Positive integer: The number of latent states associated with this block (2).

  • t Positive integer: The number of time steps associated with this block. If 1, the block is compatible with blocks of any time length, but if t is greater than 1, this block can only be used with blocks of the same time length.

  • k Positive integer: The number of outcomes associated with this block. This block can only be used with blocks with the same outcome length.

  • pred.names Vector: The name of the linear predictors associated with this block.

  • monitoring Vector: The combination of monitoring, monitoring and monitoring.pulse.

  • type Character: The type of block (Noise).

Details

For the details about the implementation see dos Santos et al. (2024) .

For the details about dynamic regression models in the context of DLMs, see West and Harrison (1997) , chapters 6 and 9.

References

Junior, Silvaneo Vieira dos Santos, Mariane Branco Alves, Helio S. Migon (2024). “kDGLM: an R package for Bayesian analysis of Dynamic Generialized Linear Models.”

Mike West, Jeff Harrison (1997). Bayesian Forecasting and Dynamic Models (Springer Series in Statistics). Springer-Verlag. ISBN 0387947256.

See also

fit_model

Other auxiliary functions for structural blocks: TF_block(), block_mult(), block_rename(), block_superpos(), harmonic_block(), intervention(), polynomial_block(), regression_block(), specify.dlm_block(), summary.dlm_block()

Examples


noise_block(mu = 1, D = 0.99, R1 = 1e-2)
#> Noise DLM block.
#> latent states: 
#>     Noise: Var (1 variable(s))
#> 
#> Linear predictors: 
#>     mu
#> 
#> Status: defined
#> Serie length: 1
#> Interventions at: 
#> Number of latent states: 1
#> Number of linear predictors: 1