| summary.lme {nlme} | R Documentation |
Additional information about the linear mixed-effects fit represented
by object is extracted and included as components of
object. The returned object is suitable for printing with the
print.summary.lme method.
## S3 method for class 'lme': summary(object, adjustSigma, verbose, ...)
object |
an object inheriting from class lme, representing
a fitted linear mixed-effects model. |
adjustSigma |
an optional logical value. If TRUE and the
estimation method used to obtain object was maximum
likelihood, the residual standard error is multiplied by
sqrt(nobs/(nobs - npar)),
converting it to a REML-like estimate. This argument is only used
when a single fitted object is passed to the function. Default is
TRUE. |
verbose |
an optional logical value used to control the amount of
output in the print.summary.lme method. Defaults to
FALSE. |
... |
some methods for this generic require additional arguments. None are used in this method. |
an object inheriting from class summary.lme with all components
included in object (see lmeObject for a full
description of the components) plus the following components:
corFixed |
approximate correlation matrix for the fixed effects estimates |
tTable |
a data frame with columns Value,
Std. Error, DF, t-value, and p-value representing
respectively the fixed effects estimates, their approximate standard
errors, the denominator degrees of freedom, the ratios between the
estimates and their standard errors,
and the associated p-value from a t distribution. Rows
correspond to the different fixed effects. |
residuals |
if more than five observations are used in the
lme fit, a vector with the minimum, first quartile, median, third
quartile, and maximum of the innermost grouping level residuals
distribution; else the innermost grouping level residuals. |
AIC |
the Akaike Information Criterion corresponding to
object. |
BIC |
the Bayesian Information Criterion corresponding to
object. |
Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
lme, AIC, BIC,
print.summary.lme
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject) summary(fm1)