Tidy a blavaan object — tidy.blavaan • blavaan

This function repackages the output from summary() into a friendly data.frame.

Usage

# S3 method for class 'blavaan'
tidy(
  x,
  estimate.method = c("mean", "median", "mode"),
  conf.int = TRUE,
  rhat = TRUE,
  ess = TRUE,
  priors = TRUE,
  ...
)

Arguments

x: A blavaan object.
estimate.method: The posterior summary statistic to compute the point estimates. One of "mean" (posterior mean, default), "median" (posterior median), or "mode" (posterior mode). Only free parameters are summarized by the requested statistic; fixed parameters retain their fixed values. Defined/constrained parameters (:=) keep the point estimate from the standard summary() output regardless of estimate.method.
conf.int: Logical indicating whether to include credible intervals. Default is TRUE.
rhat: Logical indicating whether to include the Rhat convergence diagnostic. Default is TRUE.
ess: Logical indicating whether to include the effective sample size. Default is TRUE.
priors: Logical indicating whether to include the prior distribution specification. Default is TRUE.
...: Additional arguments (currently ignored).

Value

A data.frame with columns:

term: Parameter name (lhs, op, rhs combined)
op: Operator from the model syntax
level: The level of a parameter estimate
group: Group number (for multigroup models)
estimate: Posterior summary statistic determined by estimate.method
std.error: Posterior standard deviation
conf.low: Lower bound of 95% credible interval (if conf.int = TRUE)
conf.high: Upper bound of 95% credible interval (if conf.int = TRUE)
std.lv: Standardized estimates based on the variances of the (continuous) latent variables only
std.all: Standardized estimates based on both the variances of both (continuous) observed and latent variables.
rhat: Rhat convergence diagnostic (if rhat = TRUE)
ess: Effective sample size (if ess = TRUE)
prior: Prior distribution specification (if priors = TRUE)

Examples

if (FALSE) { # \dontrun{
data(HolzingerSwineford1939, package = "lavaan")

HS.model <- 'visual =~ x1 + x2 + x3'
fit <- bcfa(HS.model, data = HolzingerSwineford1939, seed = 123,
            n.chains = 1, sample = 300)
tidy(fit)
tidy(fit, estimate.method = "median")

data(Demo.twolevel, package = "lavaan")
model <- "
    level: within
        fw =~ y1 + y2 + y3
        fw ~ x1 + x2 + x3
    level: between
        fb =~ y1 + y2 + y3
        fb ~ w1 + w2
"
bfit <- bsem(
  model = model,
  data = Demo.twolevel,
  cluster = "cluster",
  seed = 123,
  n.chains = 1,
  sample = 300,
  target = "stan"
)
tidy(fit)
} # }