broom (version 0.5.0)

tidy.lavaan: Tidy a(n) lavaan object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for lavaan
tidy(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

x

A lavaan object, such as those return from lavaan::cfa(), and lavaan::sem().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

Additional arguments passed to lavaan::parameterEstimates(). Cautionary note: Misspecified arguments may be silently ignored.

Value

A tibble::tibble with one row for each estimated parameter and columns:

term

The result of paste(lhs, op, rhs)

op

The operator in the model syntax (e.g. ~~ for covariances, or ~ for regression parameters)

group

The group (if specified) in the lavaan model

estimate

The parameter estimate (may be standardized)

std.error

statistic

The z value returned by lavaan::parameterEstimates()

p.value

conf.low

conf.high

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.

std.nox

Standardized estimates based on both the variances of both (continuous) observed and latent variables, but not the variances of exogenous covariates.

See Also

tidy(), lavaan::cfa(), lavaan::sem(), lavaan::parameterEstimates()

Other lavaan tidiers: glance.lavaan

Examples

# NOT RUN {
if (require("lavaan")) {

 library(lavaan)
 
 cfa.fit <- cfa('F =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9',
                data = HolzingerSwineford1939, group = "school")
 tidy(cfa.fit)
}

# }

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