merMod (linear, generalized or
nonlinear). The within-group error variance and standard
deviation are also calculated.
"VarCorr"(x, sigma=1, ...)
"as.data.frame"(x, row.names = NULL, optional = FALSE, order = c("cov.last", "lower.tri"), ...)
"print"(x, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", formatter = format, ...)VarCorr: a fitted model object, usually an object inheriting from
class merMod. For as.data.frame, a
VarCorr.merMod object returned from VarCorr."cov.last"), or in the order of the lower triangle of the
variance-covariance matrix ("lower.tri")?as.data.frame method.as.data.frame method; passed to
other print() methods for the print() method.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:
NA for variance parameters)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.
lmer, nlmer
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")
Run the code above in your browser using DataLab