scale.model {INLA} | R Documentation |
This function scales an intrinsic GMRF model so the geometric mean of the marginal variances is one
inla.scale.model(Q, constr = NULL, eps = sqrt(.Machine$double.eps))
Q |
A SPD matrix, either as a (dense) matrix or |
constr |
Linear constraints spanning the null-space of |
eps |
A small constant added to the diagonal of |
inla.scale.model
returns a sparseMatrix
of type dgTMatrix
scaled so the geometric mean of the marginal variances (of the possible
non-singular part of Q
) is one, for each connected component of the matrix.
Havard Rue hrue@r-inla.org
## Q is singular data(Germany) g = system.file("demodata/germany.graph", package="INLA") Q = -inla.graph2matrix(g) diag(Q) = 0 diag(Q) = -rowSums(Q) n = dim(Q)[1] Q.scaled = inla.scale.model(Q, constr = list(A = matrix(1, 1, n), e=0)) print(diag(MASS::ginv(Q.scaled))) ## Q is singular with 3 connected components g = inla.read.graph("6 1 2 2 3 2 2 1 3 3 2 1 2 4 1 5 5 1 4 6 0") print(paste("Number of connected components", g$cc$n)) Q = -inla.graph2matrix(g) diag(Q) = 0 diag(Q) = -rowSums(Q) n = dim(Q)[1] Q.scaled = inla.scale.model(Q, constr = list(A = matrix(1, 1, n), e=0)) print(diag(MASS::ginv(Q.scaled))) ## Q is non-singular with 3 connected components. no constraints needed diag(Q) = diag(Q) + 1 Q.scaled = inla.scale.model(Q) print(diag(MASS::ginv(Q.scaled)))