base_block function

Basic structural blocks

Creates the basic structure for a dlm block with desired order.

base_block(..., order, name, D, h, H, a1, R1, monitoring)

Arguments

• ...: Named values for the planning matrix.
• order: integer: The order of the structure. Must be positive
• name: character: 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: array, matrix, vector or scalar: The values for the discount factors associated with the latent states at each time. If D is an array, its dimensions should be n x n x t, where n is the order of the polynomial block and t is the length of the outcomes. If D is a matrix, its dimensions should be n x n and the same discount matrix will be used in all observations. If D is a vector, it should have size t and it is interpreted as the discount factor at each observed time (same discount for all variable). If D is a scalar, the same discount will be used for all latent states at all times.
• h: matrix, vector or scalar: A drift to be add after the temporal evolution (can be interpreted as the mean of the random noise at each time). If a matrix, its dimension should be n x t, where n is the number of latent states (i.e., the order) and t is the length of the series. If a vector, it should have size t, and each value will be applied to the first latent state (the one which affects the linear predictors) in their respective time. If a scalar, the passed value will be used for the first latent state at each time.
• H: array, matrix, vector or scalar: The values for the covariance matrix for the noise factor at each time. If H is an array, its dimensions should be n x n x t, where n is the order of the polynomial block and t is the length of the series. If H is a matrix, its dimensions should be n x n and its values will be used for each time. If H is a vector or scalar, a discount factor matrix will be created as a diagonal matrix with the values of H in the diagonal.
• a1: vector or scalar: The prior mean for the latent states associated with this block at time 1. If a1 is a vector, its dimension should be equal to the order of the polynomial block. If a1 is a scalar, its value will be used for all latent states.
• R1: matrix, vector or scalar: The prior covariance matrix for the latent states associated with this block at time 1. If R1 is a matrix, its dimensions should be n x n. If R1 is a vector or scalar, a covariance matrix will be created as a diagonal matrix with the values of R1 in the diagonal.
• monitoring: vector: A vector of flags indicating which variables should be monitored (if automated monitoring is used). Its size should be n. The default is that only the first order component of this structure should be monitored.