Maximum likelihood estimation of spatial simultaneous autoregressive
error models of the form:$$y = X \beta + u, u = \lambda W u + \varepsilon$$
where $$ is found by optimize() first and $$ and other
parameters by generalized least squares subsequently.
errorsarlm(formula, data=list(), listw, method="eigen", quiet=TRUE,
zero.policy=FALSE, tol.solve=1.0e-7, tol.opt=.Machine$double.eps^0.5,
sparsedebug=FALSE)
sar.error.f(lambda, eig, y, wy, x, WX, n, quiet)
sar.error.f.s(lambda, sn, y, wy, x, WX, n, quiet, sparsedebug)- formula
{a symbolic description of the model to be fit. The details
of model specification are given for lm()}
- data
{an optional data frame containing the variables in the model.
By default the variables are taken from the environment which the function
is called.}
- listw
{a listw object created for example by nb2listw}
- method
{"eigen" (default) - the Jacobian is computed as the product
of (1 - rho*eigenvalue) using eigenw
, and "sparse" - computes the
determinant of the sparse matrix (I - rho*W) directly using log.spwdet.
}
- quiet
{default=TRUE; if FALSE, reports function values during optimization.}
- zero.policy
{if TRUE assign zero to the lagged value of zones without
neighbours, if FALSE (default) assign NA - causing errorsarlm() to terminate with an error}
- tol.solve
{the tolerance for detecting linear dependencies in the columns of matrices to be inverted - passed to solve() (default=1.0e-7)}
- tol.opt
{the desired accuracy of the optimization - passed to optimize() (default=square root of double precision machine tolerance)}
- sparsedebug
{if TRUE, writes a log file on sparse matrix operations
(name sparsestats) in the current directory. To be used if sparse estimation
fails!}
- lambda
{value of the spatial parameter}
- eig
{eigenvalues of the full spatial weights matrix from eigenw}
- y
{dependent variable}
- wy
{spatially lagged dependent variable}
- x
{independent variables}
- WX
{spatially lagged independent variables}
- n
{length of y (and eig)}
- sn
{sparse spatial neighbour object from listw2sn}
The asymptotic standard error of $$ is only computed when
method=eigen, because the full matrix operations involved would be costly
for large n typically associated with the choice of method=sparse.
When using the sparse method, the user takes (unfortunately) full
responsibility for possible failures, including R terminating with
a core dump! Safeguards have been put in place to try to trap errant
behaviour in the sparse functions' memory allocation, but they may
not always help. When sparsedebug is TRUE, a log file (sparsestats)
is written in the working directory - the figure of interest is the
number of allocated blocks. At present, spwdet will fail when
this increases over the number initially allocated, but will not release
memory allocated by the sparse functions. In the event of problems,
save your workspace and quit R. Problems seem to be related to larger
n, and to an unknown trigger precipitating incontrolled fillin, in the
course of which the sparse routines lose track of their memory pointers,
and then provoke a segmentation fault trying to free unallocated memory.
A list object of class sarlm
- type
{"error"}
- lambda
{simultaneous autoregressive error coefficient}
- coefficients
{GLS coefficient estimates}
- rest.se
{GLS coefficient standard errors (are equal to asymptotic
standard errors)}
- LL
{log likelihood value at computed optimum}
- s2
{GLS residual variance}
- SSE
{sum of squared GLS errors}
- parameters
{number of parameters estimated}
- lm.model
{the lm object returned when estimating for $=0$
- method
{the method used to calculate the Jacobian}
- call
{the call used to create this object}
- residuals
{GLS residuals}
- lm.target
{the lm object returned for the GLS fit}
- fitted.values
{Difference between residuals and response variable}
- ase
{TRUE if method=eigen}
- formula
{model formula}
- se.fit
{Not used yet}
- lambda.se
{if ase=TRUE, the asymptotic standard error of $$
- LMtest
{NULL for this model}
- zero.policy
{zero.policy for this model}
The sar.error.* functions return the value of the log likelihood function
at $$.
Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion;
Ord, J. K. 1975 Estimation methods for models of spatial interaction,
Journal of the American Statistical Association, 70, 120-126;
Anselin, L. 1988 Spatial econometrics: methods and models.
(Dordrecht: Kluwer); Anselin, L. 1995 SpaceStat, a software program for
the analysis of spatial data, version 1.80. Regional Research Institute,
West Virginia University, Morgantown, WV (www.spacestat.com);
Anselin L, Bera AK (1998) Spatial dependence in linear regression models
with an introduction to spatial econometrics. In: Ullah A, Giles DEA
(eds) Handbook of applied economic statistics. Marcel Dekker, New York,
pp. 237-289.
[object Object],[object Object],[object Object]
lm, lagsarlm,
eigenw, log.spwdet, predict.sarlm,
residuals.sarlm
data(oldcol)
COL.err.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
nb2listw(COL.nb), method="eigen", quiet=FALSE)
COL.err.sp <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
nb2listw(COL.nb), method="sparse", quiet=FALSE)
summary(COL.err.eig)
summary(COL.err.sp)
spatial