This function calculates the estimated standard deviations,
correlations and covariances of the group-level terms
in a multilevel model of class brmsfit.
For linear models, the residual standard deviations,
correlations and covariances are also returned.
# S3 method for brmsfit
VarCorr(x, sigma = 1, summary = TRUE, robust = FALSE,
probs = c(0.025, 0.975), old = FALSE, estimate = "mean", ...)# S3 method for brmsVarCorr
as.data.frame(x, ...)
An object of class brmsfit.
Ignored (included for compatibility with
VarCorr).
Should summary statistics
(i.e. means, sds, and 95% intervals) be returned
instead of the raw values? Default is TRUE.
If FALSE (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If TRUE, the median and the
median absolute deivation (MAD) are applied instead.
Only used if summary is TRUE.
The percentiles to be computed by the quantile
function. Only used if summary is TRUE.
Logical; indicates if the old implementation
of this method (prior to version 1.7.0) should be used.
Defaults to FALSE.
(Deprecated) A character vector specifying
which coefficients (e.g., "mean", "median",
"sd", or "quantile") should be calculated
for the population-level effects. Only used if old
is TRUE.
Further arguments to be passed to the functions
specified in estimate
A list of lists (one per grouping factor), each with three elements: a matrix containing the standard deviations, an array containing the correlation matrix, and an array containing the covariance matrix with variances on the diagonial.
If old is TRUE, the returned object is of class
brmsVarCorr, which can be coerced to a data.frame
by using the as.data.frame method.
# NOT RUN {
fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1+Trt_c|visit),
data = epilepsy, family = gaussian(), chains = 2)
VarCorr(fit)
# }
# NOT RUN {
# }
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