An object returned by the
gls function, inheriting from class
gls
and representing a generalized least squares fitted linear
model. Objects of this class have methods for the generic functions
anova
,
coef,
fitted,
formula,
getGroups
,
getResponse,
intervals,
logLik,
plot
,
predict,
print,
residuals,
summary
, and
update.
VALUE:
The following components must be included in a legitimate
gls
object.
apVar
an approximate covariance matrix for the
variance-covariance coefficients. If
apVar = FALSE in the list
of control values used in the call to
gls, this
component is equal to
NULL.
call
a list containing an image of the
gls call that
produced the object.
coefficients
a vector with the estimated linear model
coefficients.
contrasts
a list with the contrasts used to represent factors
in the model formula. This information is important for making
predictions from a new data frame in which not all levels of the
original factors are observed. If no factors are used in the model,
this component will be an empty list.
dims
a list with basic dimensions used in the model fit,
including the components
N - the number of observations in
the data and
p - the number of coefficients in the linear
model.
fitted
a vector with the fitted values..
glsStruct
an object inheriting from class
glsStruct,
representing a list of linear model components, such as
corStruct
and
varFunc objects.
groups
a vector with the correlation structure grouping factor,
if any is present.
logLik
the log-likelihood at convergence.
method
the estimation method: either
"ML" for maximum
likelihood, or
"REML" for restricted maximum likelihood.
numIter
the number of iterations used in the iterative
algorithm.
residuals
a vector with the residuals.
sigma
the estimated residual standard error.
varBeta
an approximate covariance matrix of the
coefficients estimates.