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, …)
```

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.

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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