From Ord's 1975 paper, it is known that the Jacobian for SAR models may be found by "symmetrizing" by similarity (the eigenvalues of similar matrices are identical, so the Jacobian is too). This applies only to styles "W" and "S" with underlying symmetric binary neighbour relations or symmetric general neighbour relations (so no k-nearest neighbour relations). The function is invoked automatically within the SAR fitting functions, to call eigen on a symmetric matrix for the default eigen method, or to make it possible to use the Matrix method on weights that can be "symmetrized" in this way.

similar.listw(listw)

Arguments

listw

a listw object created for example by nb2listw

Value

a listw object

References

Ord, J. K. 1975 Estimation methods for models of spatial interaction, Journal of the American Statistical Association, 70, 120-126

Author

Roger Bivand Roger.Bivand@nhh.no

See also

Examples

#require("spdep", quietly=TRUE)
data(oldcol, package="spdep")
COL.W <- spdep::nb2listw(COL.nb, style="W")
COL.S <- spdep::nb2listw(COL.nb, style="S")
sum(log(1 - 0.5 * eigenw(COL.W)))
#> [1] -1.62766
sum(log(1 - 0.5 * eigenw(similar.listw(COL.W))))
#> [1] -1.62766
W_J <- as(as_dsTMatrix_listw(similar.listw(COL.W)), "CsparseMatrix")
I <- as_dsCMatrix_I(dim(W_J)[1])
c(determinant(I - 0.5 * W_J, logarithm=TRUE)$modulus)
#> [1] -1.62766
sum(log(1 - 0.5 * eigenw(COL.S)))
#> [1] -1.602757
sum(log(1 - 0.5 * eigenw(similar.listw(COL.S))))
#> [1] -1.602757
W_J <- as(as_dsTMatrix_listw(similar.listw(COL.S)), "CsparseMatrix")
c(determinant(I - 0.5 * W_J, logarithm=TRUE)$modulus)
#> [1] -1.602757