spdep (version 0.5-9)

do_ldet: Spatial regression model Jacobian computations

Description

These functions are made available in the package namespace for other developers, and are not intended for users. They provide a shared infrastructure for setting up data for Jacobian computation, and then for caclulating the Jacobian, either exactly or approximately, in maximum likelihood fitting of spatial regression models. The techniques used are the exact eigenvalue, Cholesky decompositions (Matrix, spam), and LU ones, with Chebyshev and Monte Carlo approximations.

Usage

do_ldet(coef, env, which=1)
cheb_setup(env, q=5, which=1)
mcdet_setup(env, p=16, m=30, which=1)
eigen_setup(env, which=1)
spam_setup(env, which=1)
Matrix_setup(env, Imult, super, which=1)
LU_setup(env, which=1)

Arguments

coef

spatial coefficient value

env

environment containing pre-computed objects, fixed after assignment in setup functions

which

default 1; if 2, use second listw object

q

Chebyshev approximation order; default in calling spdep functions is 5, here it cannot be missing and does not have a default

p

Monte Carlo approximation number of random normal variables; default calling spdep functions is 16, here it cannot be missing and does not have a default

m

Monte Carlo approximation number of series terms; default in calling spdep functions is 30, here it cannot be missing and does not have a default

Imult

see Cholesky; numeric scalar which defaults to zero. The matrix that is decomposed is A+m*I where m is the value of Imult and I is the identity matrix of order ncol(A). Default in calling spdep funct

super

see Cholesky; logical scalar indicating is a supernodal decomposition should be created. The alternative is a simplicial decomposition. Default in calling spdep functions is FALSE, here it cannot be

Value

• do_ldet returns the value of the Jacobian for the calculation method recorded in the environment argument; the remaining functions modify the environment in place as a side effect and return nothing.

Details

Since environments are containers in the R workspace passed by reference rather than by value, they are useful for passing objects to functions called in numerical optimisation, here for the maximum likelihood estimation of spatial regression models. This technique can save a little time on each function call, balanced against the need to access the objects in the environment inside the function. The environment should contain a family string object either SAR, CAR or SMA (used in do_ldet to choose spatial moving average in spautolm, and these specific objects before calling the set-up functions:

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Some set-up functions may also assign similar to the environment of the weights were made symmetric by similarity.

References

LeSage J and RK Pace (2009) Introduction to Spatial Econometrics. CRC Press, Boca Raton, pp. 77--110

See Also

spautolm, lagsarlm, errorsarlm, Cholesky

Examples

data(boston)
lw <- nb2listw(boston.soi)
can.sim <- spdep:::can.be.simmed(lw)
env <- new.env(parent=globalenv())
assign("listw", lw, envir=env)
assign("can.sim", can.sim, envir=env)
assign("similar", FALSE, envir=env)
assign("verbose", FALSE, envir=env)
assign("family", "SAR", envir=env)
eigen_setup(env)
get("similar", envir=env)
do_ldet(0.5, env)
rm(env)
env <- new.env(parent=globalenv())
assign("listw", lw, envir=env)
assign("can.sim", can.sim, envir=env)
assign("similar", FALSE, envir=env)
assign("family", "SAR", envir=env)
assign("n", length(boston.soi), envir=env)
Matrix_setup(env, Imult=2, super=FALSE)
get("similar", envir=env)
do_ldet(0.5, env)
rm(env)
env <- new.env(parent=globalenv())
assign("listw", lw, envir=env)
assign("n", length(boston.soi), envir=env)
assign("can.sim", can.sim, envir=env)
assign("similar", FALSE, envir=env)
assign("family", "SAR", envir=env)
spam_setup(env)
get("similar", envir=env)
do_ldet(0.5, env)
rm(env)
env <- new.env(parent=globalenv())
assign("listw", lw, envir=env)
assign("n", length(boston.soi), envir=env)
assign("similar", FALSE, envir=env)
assign("family", "SAR", envir=env)
LU_setup(env)
get("similar", envir=env)
do_ldet(0.5, env)
rm(env)
env <- new.env(parent=globalenv())
assign("listw", lw, envir=env)
assign("similar", FALSE, envir=env)
assign("family", "SAR", envir=env)
cheb_setup(env, q=5)
get("similar", envir=env)
do_ldet(0.5, env)
rm(env)
env <- new.env(parent=globalenv())
assign("listw", lw, envir=env)
assign("n", length(boston.soi), envir=env)
assign("similar", FALSE, envir=env)
assign("family", "SAR", envir=env)
set.seed(12345)
mcdet_setup(env, p=16, m=30)
get("similar", envir=env)
do_ldet(0.5, env)
rm(env)