glsObject: Fitted gls Object
Description
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. - apVaran 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. - calla list containing an image of the
gls call that
produced the object. - coefficientsa vector with the estimated linear model
coefficients.
- contrastsa 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.
- dimsa 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. - fitteda vector with the fitted values..
- glsStructan object inheriting from class
glsStruct,
representing a list of linear model components, such as
corStruct and varFunc objects. - groupsa vector with the correlation structure grouping factor,
if any is present.
- logLikthe log-likelihood at convergence.
- methodthe estimation method: either
"ML" for maximum
likelihood, or "REML" for restricted maximum likelihood. - numIterthe number of iterations used in the iterative
algorithm.
- residualsa vector with the residuals.
- sigmathe estimated residual standard error.
- varBetaan approximate covariance matrix of the
coefficients estimates.