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 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:

## 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)
#> This is lavaan 0.6-17
#> lavaan is FREE software! Please report any bugs.
# fit model
cfa.fit <- cfa(
"F =~ x1 + x2 + x3 + x4 + x5",
data = HolzingerSwineford1939, group = "school"
)
# summarize model fit with tidiers
glance(cfa.fit)
#> # A tibble: 1 × 17
#> agfi AIC BIC cfi chisq npar rmsea rmsea.conf.high srmr tli
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.971 4473. 4584. 0.766 99.3 30 0.244 0.288 0.115 0.533
#> # ℹ 7 more variables: converged <lgl>, estimator <chr>, ngroups <int>,
#> # missing_method <chr>, nobs <int>, norig <int>, nexcluded <int>
```