Summary Method for Spatial Linear Models

DESCRIPTION:

Returns a summary list for a spatial linear model. A null value will be returned if printing is invoked.

USAGE:

summary.slm(object, correlation=T) 

REQUIRED ARGUMENTS:

object
the fitted model. This is assumed to be the result of some fit that produces an object inheriting from the class "slm", in the sense that the components returned by the slm function will be available.

OPTIONAL ARGUMENTS:

correlation
if TRUE, then the variance/covariance and correlation matrices for the coefficients is included in the summary.

VALUE:

a list with the following components:
call
the function call used to compute the summarized object.
terms
the terms in the linear model.
parameters
the estimates of the model covariance matrix parameters. A spatial model consists of a specification of the large scale variation (the means), and a specification of the covariance structure of the observed spatial data. Elements in parameters are estimates of parameters used in the specification of this covariance structure.
iterations
the total number of iterations used in solving for the covariance matrix parameters.
grad.norm
the final gradient norm in the iterations.
objective
the minimum objective (minus log likelihood) obtained in the iterations.
message
the convergence message.
residuals
the residuals from the fit. These are not ordinary residuals. See the cov.family.object help file or the MA, CAR and SAR help files for more information.
coefficients
parameters estimated for the linear model, along with standard error estimates, test statistics (that the coefficient is zero), and a two-sided significance level.
sigma
the residual standard error estimate.
df
the number of degrees of freedom for the model and for residuals.
cov.coef
the computed variance/covariance matrix for the estimated linear model coefficients. The parameters estimated for the model covariance matrix are not included.

DETAILS:

This function is a method for the generic function for class slm. It can be invoked by calling for an object x of the appropriate class, or directly by calling regardless of the class of the object.

SEE ALSO:

, .

EXAMPLES:

sfit <- slm(sid.ft~nwbirths.ft, cov.family=SAR, data=sids, 
  spatial.arglist=list(neighbor=sids.neighbor)) 
summary(sfit)