W_sampler {estimateW} | R Documentation |
An R6 class for sampling the elements of
Description
An R6 class for sampling the elements of
An R6 class for sampling the elements of
Format
An R6Class
generator object
Details
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).
Public fields
W_prior
The current
W_priors
curr_w
numeric, non-negative
by
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
by
spatial connectivity matrix
curr_A
The current spatial projection matrix
.
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
. Set while invoking
initialize
or using the functionset_rho
.
Methods
Public methods
Method new()
Usage
W_sampler$new(W_prior, curr_rho = NULL)
Arguments
W_prior
The list returned by
W_priors
curr_rho
optional single number between -1 and 1. Value of the spatial autoregressive parameter
. Defaults to NULL, in which case no updates of the log-determinant, the spatial projection matrix, and its inverse are carried out.
Method set_rho()
If the spatial autoregressive parameter is updated during the sampling procedure the log determinant, the
spatial projection matrix
and it's inverse must be updated. This function should be
used for a consistent update. At least the new scalar value for
must be supplied.
Usage
W_sampler$set_rho(new_rho, newLogdet = NULL, newA = NULL, newAI = NULL)
Arguments
new_rho
single, number; must be between -1 and 1.
newLogdet
An optional value for the log determinant corresponding to
newW
andcurr_rho
newA
An optional value for the spatial projection matrix using
newW
andcurr_rho
newAI
An optional value for the matrix inverse of
newA
Method sample()
Usage
W_sampler$sample(Y, curr_sigma, mu, lag_mu = matrix(0, nrow(tY), ncol(tY)))
Arguments
Y
The
by
matrix of responses
curr_sigma
The variance parameter
mu
The
by
matrix of means.
lag_mu
by
matrix of means that will be spatially lagged with the estimated
. Defaults to a matrix with zero elements.
References
Krisztin, T., and Piribauer, P. (2022) A Bayesian approach for the estimation of weight matrices in spatial autoregressive models. Spatial Economic Analysis, 1-20.