lme4: VarCorr – R documentation

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VarCorr

Extract Variance and Correlation Components

Description

This function calculates the estimated variances, standard deviations, and correlations between the random-effects terms in a mixed-effects model, of class merMod (linear, generalized or nonlinear). The within-group error variance and standard deviation are also calculated.

Usage

## S3 method for class 'merMod'
VarCorr(x, sigma=1, ...)


## S3 method for class 'VarCorr.merMod'
as.data.frame(x, row.names = NULL,
              optional = FALSE, order = c("cov.last", "lower.tri"), ...)
## S3 method for class 'VarCorr.merMod'
print(x, digits = max(3, getOption("digits") - 2),
      comp = "Std.Dev.", formatter = format, ...)

Arguments

`x`	for `VarCorr`: a fitted model object, usually an object inheriting from class `merMod`. For `as.data.frame`, a `VarCorr.merMod` object returned from `VarCorr`.
`sigma`	an optional numeric value used as a multiplier for the standard deviations.
`digits`	an optional integer value specifying the number of digits
`order`	arrange data frame with variances/standard deviations first and covariances/correlations last for each random effects term (`"cov.last"`), or in the order of the lower triangle of the variance-covariance matrix (`"lower.tri"`)?
`row.names, optional`	Ignored: necessary for the `as.data.frame` method.
`...`	Ignored for the `as.data.frame` method; passed to other `print()` methods for the `print()` method.

`comp`	a `character` vector, specifying the components to be printed; simply passed to `formatVC()`.
`formatter`	a `function` for formatting the numbers; simply passed to `formatVC()`.

Details

The print method for VarCorr.merMod objects has optional arguments digits (specify digits of precision for printing) and comp: the latter is a character vector with any combination of "Variance" and "Std.Dev.", to specify whether variances, standard deviations, or both should be printed.

Value

An object of class VarCorr.merMod. The internal structure of the object is a list of matrices, one for each random effects grouping term. For each grouping term, the standard deviations and correlation matrices for each grouping term are stored as attributes "stddev" and "correlation", respectively, of the variance-covariance matrix, and the residual standard deviation is stored as attribute "sc" (for glmer fits, this attribute stores the scale parameter of the model).

The as.data.frame method produces a combined data frame with one row for each variance or covariance parameter (and a row for the residual error term where applicable) and the following columns:

grp: grouping factor
var1: first variable
var2: second variable (NA for variance parameters)
vcov: variances or covariances
sdcor: standard deviations or correlations

Author(s)

This is modeled after VarCorr from package nlme, by Jose Pinheiro and Douglas Bates.

Examples

data(Orthodont, package="nlme")
fm1 <- lmer(distance ~ age + (age|Subject), data = Orthodont)
(vc <- VarCorr(fm1))  ## default print method: standard dev and corr
## both variance and std.dev.
print(vc,comp=c("Variance","Std.Dev."),digits=2)
## variance only
print(vc,comp=c("Variance"))
as.data.frame(vc)
as.data.frame(vc,order="lower.tri")

lme4

Linear Mixed-Effects Models using 'Eigen' and S4

v1.1-26

GPL (>= 2)

Authors

Douglas Bates [aut] (<https://orcid.org/0000-0001-8316-9503>), Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>), Steven Walker [aut] (<https://orcid.org/0000-0002-4394-9078>), Rune Haubo Bojesen Christensen [ctb] (<https://orcid.org/0000-0002-4494-3399>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Bin Dai [ctb], Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Gabor Grothendieck [ctb], Peter Green [ctb] (<https://orcid.org/0000-0002-0238-9852>), John Fox [ctb], Alexander Bauer [ctb], Pavel N. Krivitsky [ctb, cph] (<https://orcid.org/0000-0002-9101-3362>, shared copyright on simulate.formula)

Initial release