sacsarlm

a symbolic description of the model to be fit. The details 
of model specification are given for <code>lm()</code>

formula

an optional data frame containing the variables in the model. 
By default the variables are taken from the environment which the function 
is called

data

a <code>listw</code> object created for example by <code>nb2listw</code>

listw

a <code>listw</code> object created for example by <code>nb2listw</code>, if not given, set to the same spatial weights as the <code>listw</code> argument

listw2

a function (default <code>options("na.action")</code>), can also be <code>na.omit</code> or <code>na.exclude</code> with consequences for residuals and fitted values - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to <code>nb2listw</code> may be subsetted.

na.action

default "sac", may be set to "sacmixed" for the Manski model to include the spatially lagged independent variables added to X using <code>listw</code>; when "sacmixed", the lagged intercept is dropped for spatial weights style "W", that is row-standardised weights, but otherwise included

type

"eigen" (default) - the Jacobian is computed as the product 
of (1 - rho*eigenvalue) using <code>eigenw</code>, and "spam" or "Matrix" for strictly symmetric weights lists of styles "B" and "C", or made symmetric by similarity (Ord, 1975, Appendix C) if possible for styles "W" and "S", using code from the spam or Matrix packages to calculate the determinant; "LU" provides an alternative sparse matrix decomposition approach. In addition, there are "Chebyshev" and Monte Carlo "MC" approximate log-determinant methods.

method

default NULL, use !verbose global option value; if FALSE, reports function values during optimization.

quiet

default NULL, use global option value; if TRUE assign zero to the lagged value of zones without 
neighbours, if FALSE (default) assign NA - causing <code>sacsarlm()</code> to terminate with an error

zero.policy

the tolerance for detecting linear dependencies in the columns of matrices to be inverted - passed to <code>solve()</code> (default=1.0e-10). This may be used if necessary to extract coefficient standard errors (for instance lowering to 1e-12), but errors in <code>solve()</code> may constitute indications of poorly scaled variables: if the variables have scales differing much from the autoregressive coefficient, the values in this matrix may be very different in scale, and inverting such a matrix is analytically possible by definition, but numerically unstable; rescaling the RHS variables alleviates this better than setting tol.solve to a very small value

tol.solve

default NULL, can either be an integer, to divide the feasible ranges into a grid of points, or a two-column matrix of spatial coefficient values, at which to evaluate the likelihood function

llprof

default NULL, if given, vectors for each weights object of powered spatial weights matrix traces output by <code>trW</code>; when given, used in some Jacobian methods

trs1, trs2

default is NULL, search intervals for each weights object for autoregressive parameters

interval1, interval2

list of extra control arguments - see section below

control

Maximum likelihood estimation of spatial simultaneous autoregressive
&#147;SAC/SARAR&#148; models of the form:$$y = \rho W1 y + X \beta + u, u = \lambda W2 u + \varepsilon$$where $rho$ and $lambda$ are found by <code>nlminb</code> or <code>optim()</code> first, and $beta$ and other parameters by generalized least squares subsequently

spatial

A collection of functions to create spatial weights matrix
objects from polygon contiguities, from point patterns by distance and
tessellations, for summarizing these objects, and for permitting their
use in spatial data analysis, including regional aggregation by minimum
spanning tree; a collection of tests for spatial autocorrelation,
including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross
product statistic, Empirical Bayes estimates and Assunção/Reis Index,
Getis/Ord G and multicoloured join count statistics, local Moran's I
and Getis/Ord G, saddlepoint approximations  and exact tests for global
and local Moran's I; and functions for estimating spatial simultaneous
autoregressive (SAR) lag and error models, impact measures for lag
models, weighted and unweighted SAR and CAR spatial regression models,
semi-parametric and Moran eigenvector spatial filtering, GM SAR error
models, and generalized spatial two stage least squares models.

Roger Bivand

spdep

Spatial Dependence: Weighting Schemes, Statistics and Models

sacsarlm function

Control arguments

Maximum likelihood estimation of spatial simultaneous autoregressive
&#147;SAC/SARAR&#148; models of the form:

$$y = \rho W1 y + X \beta + u, u = \lambda W2 u + \varepsilon$$

where $rho$ and $lambda$ are found by <code>nlminb</code> or <code>optim()</code> first, and $beta$ and other parameters by generalized least squares subsequently

sacsarlm: Spatial simultaneous autoregressive SAC model estimation

Description

Usage

Value

Control arguments

Details

References

See Also

Examples