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.
The following components must be included in a legitimate "gls"
object.
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.
a list containing an image of the gls call that
produced the object.
a vector with the estimated linear model coefficients.
a list of the contrast matrices 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.
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.
a vector with the fitted values.
an object inheriting from class glsStruct,
representing a list of linear model components, such as
corStruct and varFunc objects.
a vector with the correlation structure grouping factor, if any is present.
the log-likelihood at convergence.
the estimation method: either "ML" for maximum
likelihood, or "REML" for restricted maximum likelihood.
the number of iterations used in the iterative algorithm.
a vector with the residuals.
the estimated residual standard error.
an approximate covariance matrix of the coefficients estimates.
José Pinheiro and Douglas Bates bates@stat.wisc.edu
gls, glsStruct