Random-effect variances and correlations

Summarize the random-effect covariance of a fit: the standard deviation of each random-effect coefficient, the correlations between them within a term, and the covariance matrix itself.

Whether the covariance was estimated, sampled, or merely conditioned on is reported alongside it, because the three are different claims. A fit from mode = "laplace" conditions on sigma_re, so its values are the ones supplied; mode = "eb" estimates them; a sampler tier integrates them.

Usage

VarCorr(x, sigma = 1, ...)

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

Arguments

x

A tulpa_fit.

sigma

Ignored, for compatibility with the VarCorr generic.

...

Ignored.

Value

A data frame with one row per random-effect coefficient: term, coef, sd, and source (one of "estimated", "sampled", "conditioned"). Correlated terms additionally carry the covariance matrices in the "cov" attribute, one per term, each with a "correlation" attribute. Returns an empty data frame when the fit has no random effects.

See also

ranef() for the per-level deviations, tulpa_eb() to estimate the covariance rather than condition on it.

Examples

# \donttest{
set.seed(1)
G <- 30L; per <- 10L; n <- G * per
grp <- rep(seq_len(G), each = per); x <- rnorm(n)
b <- rnorm(G, 0, 0.8)
d <- data.frame(y = rpois(n, exp(0.3 + 0.5 * x + b[grp])), x = x,
                g = factor(grp))
fit <- tulpa(y ~ x + (1 | g), data = d, family = "poisson", mode = "eb")
VarCorr(fit)
# }