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 "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.
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
.
# NOT RUN {
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
(s1 <- summary(fm1))
# }
# NOT RUN {
coef(s1) # the (coef | Std.E | t | P-v ) matrix
# }
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