glance.lavaan: Glance at a(n) lavaan object

In broom: Convert Statistical Objects into Tidy Tibbles

Glance at a(n) lavaan object

Description

Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

Usage

## S3 method for class 'lavaan'
glance(x, ...)

Arguments

x

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

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are: tidy() methods will warn when supplied an exponentiate argument if it will be ignored. augment() methods will warn when supplied a newdata argument if it will be ignored.

Value

A one-row tibble::tibble with columns:

chisq	Model chi squared
npar	Number of parameters in the model
rmsea	Root mean square error of approximation
rmsea.conf.high	95 percent upper bound on RMSEA
srmr	Standardised root mean residual
agfi	Adjusted goodness of fit
cfi	Comparative fit index
tli	Tucker Lewis index
AIC	Akaike information criterion
BIC	Bayesian information criterion
ngroups	Number of groups in model
nobs	Number of observations included
norig	Number of observation in the original dataset
nexcluded	Number of excluded observations
converged	Logical - Did the model converge
estimator	Estimator used
missing_method	Method for eliminating missing data

For further recommendations on reporting SEM and CFA models see Schreiber, J. B. (2017). Update to core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy, 13(3), 634-643. https://doi.org/10.1016/j.sapharm.2016.06.006

See Also

glance(), lavaan::cfa(), lavaan::sem(), lavaan::fitmeasures()

Other lavaan tidiers: tidy.lavaan()

Examples

library(lavaan)

# fit model
cfa.fit <- cfa(
  "F =~ x1 + x2 + x3 + x4 + x5",
  data = HolzingerSwineford1939, group = "school"
)

# summarize model fit with tidiers
glance(cfa.fit)

broom documentation built on Sept. 30, 2024, 9:29 a.m.