inla.spde.make.block.A: Observation matrices for mesh models.

In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Description

Constructs observation/prediction weight matrices for numerical integration schemes for regional data problems. Primarily intended for internal use by inla.spde.make.A.

Usage

inla.spde.make.block.A(A, block, n.block = max(block),
                       weights = NULL,
                       rescale = c("none", "count", "weights", "sum"))

Arguments

A	A precomputed observation/prediction matrix for locations that are to be joined.
block	Indices specifying block groupings: Entries with the same block value are joined into a single row in the resulting matrix, and the block values are the row indices.
n.block	The number of blocks.
weights	Optional scaling weights to be applied row-wise to the input A matrix.
rescale	Specifies what scaling method should be used when joining the rows of the A matrix as grouped by the block specification. 'none': Straight sum, no rescaling. 'count': Divide by the number of entries in the block. 'weights': Divide by the sum of the weight values within each block. 'sum': Divide by the resulting row sums.

Author(s)

Finn Lindgren [e-mail]

See Also

inla.spde.make.A

andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.