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 has a print and a coef method, the latter returning the coefficient's tTtable.


## S3 method for class 'lme'
summary(object, adjustSigma, verbose, ...)
## S3 method for class 'summary.lme'
print(x, verbose = FALSE, ...)



an object inheriting from class "lme", representing a fitted linear mixed-effects model.


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.


an optional logical value used to control the amount of output in the print.summary.lme method. Defaults to FALSE.


additional optional arguments passed to methods, mainly for the print method.


a "summary.lme" object.



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:


approximate correlation matrix for the fixed effects estimates.


a matrix with columns named 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.


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.


the Akaike Information Criterion corresponding to object.


the Bayesian Information Criterion corresponding to object.


José Pinheiro and Douglas Bates

See Also

AIC, BIC, lme.


fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
(s1 <- summary(fm1))
coef(s1) # the (coef | Std.E | t | P-v ) matrix

[Package nlme version 3.1-139 Index]