summary.lme {nlme}R Documentation

Summarize an lme Object


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 and Douglas Bates

See Also

lme, AIC, BIC, print.summary.lme


fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)

[Package nlme version 3.1-57 Index]