Additional information about the linear model fit represented
by
object is extracted and included as components of
object
. The returned object is suitable for printing with the
print.summary.gls
method.
USAGE:
summary(object, adjustSigma, verbose)
REQUIRED ARGUMENTS:
object
an object inheriting from class
gls, representing
a generalized least squares fitted linear model.
OPTIONAL ARGUMENTS:
adjustSigma
an optional logical value. If
TRUE and the
estimation method used to obtain
object was maximum
likelihood, the standard errors of the coefficients are multiplied
by
sqrt(nobs/(nobs - npar)),
with
nobs and
npar denoting, respectively,
the number of observations and the number of coefficients. This
converts the standard errors to REML-like estimates. Default is
TRUE.
verbose
an optional logical value used to control the amount of
output in the
print.summary.gls method. Defaults to
FALSE.
VALUE:
an object inheriting from class
summary.gls with all components
included in
object (see
glsObject for a full
description of the components) plus the following components:
corBeta
approximate correlation matrix for the coefficients
estimates
tTable
a data frame with columns
Value,
Std. Error
,
t-value, and
p-value representing
respectively the coefficients estimates, their approximate standard
errors, the ratios between the estimates and their standard errors,
and the associated p-value under a t approximation. Rows
correspond to the different coefficients.
residuals
if more than five observations are used in the
gls
fit, a vector with the minimum, first quartile, median, third
quartile, and maximum of the residuals distribution; else the
residuals.
AIC
the Akaike Information Criterion corresponding to
object
.
BIC
the Bayesian Information Criterion corresponding to
object
.