Spatial regression model Jacobian computations

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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; moments use the methods due to Martin and Smirnov/Anselin.

Usage

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do_ldet(coef, env, which=1)
jacobianSetup(method, env, con, pre_eig=NULL, trs=NULL, interval=NULL, which=1)
cheb_setup(env, q=5, which=1)
mcdet_setup(env, p=16, m=30, which=1)
eigen_setup(env, which=1)
eigen_pre_setup(env, pre_eig, which=1)
spam_setup(env, pivot="MMD", which=1)
spam_update_setup(env, in_coef=0.1, pivot="MMD", which=1)
Matrix_setup(env, Imult, super=as.logical(NA), which=1)
Matrix_J_setup(env, super=FALSE, which=1)
LU_setup(env, which=1)
LU_prepermutate_setup(env, coef=0.1, order=FALSE, which=1)
moments_setup(env, trs=NULL, m, p, type="MC", correct=TRUE, trunc=TRUE, eq7=TRUE, which=1)
SE_classic_setup(env, SE_method="LU", p=16, m=30, nrho=200, interpn=2000,
 interval=c(-1,0.999), SElndet=NULL, which=1)
SE_whichMin_setup(env, SE_method="LU", p=16, m=30, nrho=200, interpn=2000,
 interval=c(-1,0.999), SElndet=NULL, which=1)
SE_interp_setup(env, SE_method="LU", p=16, m=30, nrho=200,
 interval=c(-1,0.999), 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

method

string value, used by jacobianSetup to choose method

con

control list passed from model fitting function and parsed in jacobianSetup to set environment variables for method-specific setup

pre_eig

pre-computed eigenvalues of length n

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; m serves the same purpose in the moments method

pivot

default “MMD”, may also be “RCM” for Cholesky decompisition using spam

in_coef

fill-in initiation coefficient value, default 0.1

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 functions is 2, here it cannot be missing and does not have a default, but is rescaled for binary weights matrices in proportion to the maximim row sum in those calling functions

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 for “Matrix_J” and as.logical(NA) for “Matrix”. Setting it to NA leaves the choice to a CHOLMOD-internal heuristic

order

default FALSE; used in LU_prepermutate, note warnings given for lu method

trs

A numeric vector of m traces, as from trW

type

moments trace type, see trW

correct

default TRUE: use Smirnov correction term, see trW

trunc

default TRUE: truncate Smirnov correction term, see trW

eq7

default TRUE

use equation 7 in Smirnov and Anselin (2009), if FALSE no unit root correction

SE_method

default “LU”, alternatively “MC”; underlying lndet method to use for generating SE toolbox emulation grid

nrho

default 200, number of lndet values in first stage SE toolbox emulation grid

interval

default c(-1,0.999) if interval argument NULL, bounds for SE toolbox emulation grid

interpn

default 2000, number of lndet values to interpolate in second stage SE toolbox emulation grid

SElndet

default NULL, used to pass a pre-computed two-column matrix of coefficient values and corresponding interpolated lndet values

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:

eigen

Classical Ord eigenvalue computations - either:

listw

A listw spatial weights object

can.sim

logical scalar: can the spatial weights be made symmetric by similarity

verbose

logical scalar: legacy report print control, for historical reasons only

or:

pre_eig

pre-computed eigenvalues

and assigns to the environment:

eig

a vector of eigenvalues

eig.range

the search interval for the spatial coefficient

method

string: “eigen”

Matrix

Sparse matrix pre-computed Cholesky decomposition with fast updating:

listw

A listw spatial weights object

can.sim

logical scalar: can the spatial weights be made symmetric by similarity

and assigns to the environment:

csrw

sparse spatial weights matrix

nW

negative sparse spatial weights matrix

pChol

a “CHMfactor” from factorising csrw with Cholesky

nChol

a “CHMfactor” from factorising nW with Cholesky

method

string: “Matrix”

Matrix_J

Standard Cholesky decomposition without updating:

listw

A listw spatial weights object

can.sim

logical scalar: can the spatial weights be made symmetric by similarity

n

number of spatial objects

and assigns to the environment:

csrw

sparse spatial weights matrix

I

sparse identity matrix

super

the value of the super argument

method

string: “Matrix_J”

spam

Standard Cholesky decomposition without updating:

listw

A listw spatial weights object

can.sim

logical scalar: can the spatial weights be made symmetric by similarity

n

number of spatial objects

and assigns to the environment:

csrw

sparse spatial weights matrix

I

sparse identity matrix

pivot

string — pivot method

method

string: “spam”

spam_update

Pre-computed Cholesky decomposition with updating:

listw

A listw spatial weights object

can.sim

logical scalar: can the spatial weights be made symmetric by similarity

n

number of spatial objects

and assigns to the environment:

csrw

sparse spatial weights matrix

I

sparse identity matrix

csrwchol

A Cholesky decomposition for updating

method

string: “spam”

LU

Standard LU decomposition without updating:

listw

A listw spatial weights object

n

number of spatial objects

and assigns to the environment:

W

sparse spatial weights matrix

I

sparse identity matrix

method

string: “LU”

LU_prepermutate

Standard LU decomposition with updating (pre-computed fill-reducing permutation):

listw

A listw spatial weights object

n

number of spatial objects

and assigns to the environment:

W

sparse spatial weights matrix

lu_order

order argument to lu

pq

2-column matrix for row and column permutation for fill-reduction

I

sparse identity matrix

method

string: “LU”

MC

Monte Carlo approximation:

listw

A listw spatial weights object

and assigns to the environment:

clx

list of Monte Carlo approximation terms (the first two simulated traces are replaced by their analytical equivalents)

W

sparse spatial weights matrix

method

string: “MC”

cheb

Chebyshev approximation:

listw

A listw spatial weights object

and assigns to the environment:

trT

vector of Chebyshev approximation terms

W

sparse spatial weights matrix

method

string: “Chebyshev”

moments

moments approximation:

listw

A listw spatial weights object

can.sim

logical scalar: can the spatial weights be made symmetric by similarity

and assigns to the environment:

trs

vector of traces, possibly approximated

q12

integer vector of length 2, unit roots terms, ignored until 0.5-52

eq7

logical scalar: use equation 7

correct

logical scalar: use Smirnov correction term

trunc

logical scalar: truncate Smirnov correction term

method

string: “moments”

SE_classic

:

listw

A listw spatial weights object

n

number of spatial objects

and assigns to the environment:

detval

two column matrix of lndet grid values

method

string: “SE_classic”

SE_method

string: “LU” or “MC”

SE_whichMin

:

listw

A listw spatial weights object

n

number of spatial objects

and assigns to the environment:

detval

two column matrix of lndet grid values

method

string: “SE_whichMin”

SE_method

string: “LU” or “MC”

SE_interp

:

listw

A listw spatial weights object

n

number of spatial objects

and assigns to the environment:

fit

fitted spline object from which to predict lndet values

method

string: “SE_interp”

SE_method

string: “LU” or “MC”

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

Three set-up functions emulate the behaviour of the Spatial Econometrics toolbox (March 2010) maximum likelihood lndet grid performance. The toolbox lndet functions compute a smaller number of lndet values for a grid of coefficient values (spacing 0.01), and then interpolate to a finer grid of values (spacing 0.001). “SE_classic”, which is an implementation of the SE toolbox code, for example in f_sar.m, appears to have selected a row in the grid matrix one below the correct row when the candidate coefficient value was between 0.005 and 0.01-fuzz, always rounding the row index down. A possible alternative is to choose the index that is closest to the candidate coefficient value (“SE_whichMin”). Another alternative is to fit a spline model to the first stage coarser grid, and pass this fitted model to the log likelihood function to make a point prediction using the candidate coefficient value, rather than finding the grid index (“SE_interp”).

Value

do_ldet returns the value of the Jacobian for the calculation method recorded in the environment argument, and for the Monte Carlo approximation, returns a measure of the spread of the approximation as an “sd” attribute; the remaining functions modify the environment in place as a side effect and return nothing.

Author(s)

Roger Bivand Roger.Bivand@nhh.no

References

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

Bivand, R. S., Hauke, J., and Kossowski, T. (2013). Computing the Jacobian in Gaussian spatial autoregressive models: An illustrated comparison of available methods. Geographical Analysis, 45(2), 150-179.

See Also

spautolm, lagsarlm, errorsarlm, Cholesky

Examples

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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("verbose", FALSE, envir=env)
assign("family", "SAR", envir=env)
assign("n", length(boston.soi), envir=env)
eigen_pre_setup(env, pre_eig=eigenw(similar.listw(lw)))
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("n", length(boston.soi), envir=env)
assign("similar", FALSE, envir=env)
assign("family", "SAR", envir=env)
LU_prepermutate_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)

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