R: Summarize an lme Object

summary.lme {nlme}

R Documentation

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 class 'lme'
summary(object, adjustSigma, verbose, ...)
## S3 method for class '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(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`.
`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`.

Author(s)

José Pinheiro and Douglas Bates bates@stat.wisc.edu

Examples

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

[Package nlme version 3.1-131 Index]