summary.gls: Summarize a Generalized Least Squares 'gls' Object

In nlme: Linear and Nonlinear Mixed Effects Models

Summarize a Generalized Least Squares gls Object

Description

Additional information about the linear model fit represented by object is extracted and included as components of object.

Usage

## S3 method for class 'gls'
summary(object, verbose, ...)

Arguments

object	an object inheriting from class "gls", representing a generalized least squares fitted linear model.
verbose	an optional logical value used to control the amount of output when the object is printed. Defaults to FALSE.
...	some methods for this generic require additional arguments. None are used in this method.

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 matrix 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.

Author(s)

José Pinheiro and Douglas Bates [e-mail]

See Also

AIC, BIC, gls, summary

Examples

fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
           correlation = corAR1(form = ~ 1 | Mare))
summary(fm1)
coef(summary(fm1)) # "the matrix"

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