glsObject: Fitted gls Object

In nlme: Linear and Nonlinear Mixed Effects Models

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.

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 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.
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.
modelStruct	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.

Author(s)

José Pinheiro and Douglas Bates [e-mail]

See Also

gls, glsStruct

nlme documentation built on Nov. 27, 2023, 5:09 p.m.