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

### See Also

`AIC`, `BIC`, `lme`.

### Examples

    fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
    (s1 <- summary(fm1))
    coef(s1) # the (coef | Std.E | t | P-v ) matrix

[Package nlme version 3.1-139 Index]