W_sampler | R Documentation |
An R6 class for sampling the elements of W
An R6 class for sampling the elements of W
An R6Class
generator object
This class samples the spatial weight matrix. Use the function W_priors class for setup.
The sampling procedure relies on conditional Bernoulli posteriors outlined in Krisztin and Piribauer (2022).
W_prior
The current W_priors
curr_w
numeric, non-negative n by n spatial weight matrix with zeros
on the main diagonal. Depending on the W_priors
settings can be symmetric and/or
row-standardized.
curr_W
binary n by n spatial connectivity matrix Ω
curr_A
The current spatial projection matrix I - ρ W.
curr_AI
The inverse of curr_A
curr_logdet
The current log-determinant of curr_A
curr_rho
single number between -1 and 1 or NULL, depending on whether the sampler updates
the spatial autoregressive parameter ρ. Set while invoking initialize
or using the function set_rho
.
new()
W_sampler$new(W_prior, curr_rho = NULL)
W_prior
The list returned by W_priors
curr_rho
optional single number between -1 and 1. Value of the spatial autoregressive parameter ρ. Defaults to NULL, in which case no updates of the log-determinant, the spatial projection matrix, and its inverse are carried out.
set_rho()
If the spatial autoregressive parameter ρ is updated during the sampling procedure the log determinant, the spatial projection matrix I - ρ W and it's inverse must be updated. This function should be used for a consistent update. At least the new scalar value for ρ must be supplied.
W_sampler$set_rho(new_rho, newLogdet = NULL, newA = NULL, newAI = NULL)
new_rho
single, number; must be between -1 and 1.
newLogdet
An optional value for the log determinant corresponding to newW
and curr_rho
newA
An optional value for the spatial projection matrix using newW
and curr_rho
newAI
An optional value for the matrix inverse of newA
sample()
W_sampler$sample(Y, curr_sigma, mu, lag_mu = matrix(0, nrow(tY), ncol(tY)))
Y
The n by tt matrix of responses
curr_sigma
The variance parameter σ^2
mu
The n by tt matrix of means.
lag_mu
n by tt matrix of means that will be spatially lagged with the estimated W. Defaults to a matrix with zero elements.
Krisztin, T., and Piribauer, P. (2022) A Bayesian approach for the estimation of weight matrices in spatial autoregressive models. Spatial Economic Analysis, 1-20.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.