nlme (version 3.1-131)

summary.lme: Summarize an lme Object

Description

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.

Usage

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

Arguments

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 \(\sqrt{n_{obs}/(n_{obs} - n_{par})}\), 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.

…

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

x

a "summary.lme" object.

Value

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 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.

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.

See Also

AIC, BIC, lme.

Examples

fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
(s1 <- summary(fm1))
## Not run: coef(s1) # the (coef | Std.E | t | P-v ) matrix