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 lme
summary(object, adjustSigma, verbose, ...)
# S3 method for summary.lme
print(x, verbose = FALSE, ...)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.
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
\(\sqrt{n_{obs}/(n_{obs} - n_{par})}\),
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.
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.
José Pinheiro and Douglas Bates bates@stat.wisc.edu
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
(s1 <- summary(fm1))
coef(s1) # the (coef | Std.E | t | P-v ) matrix
stopifnot(is.matrix(coef(s1)))
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