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.

# 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` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Value

A one-row tibble::tibble with columns:

chisqModel chi squared

nparNumber of parameters in the model

rmseaRoot mean square error of approximation

rmsea.conf.high95 percent upper bound on RMSEA

srmrStandardised root mean residual

agfiAdjusted goodness of fit

cfiComparative fit index

tliTucker Lewis index

AICAkaike information criterion

BICBayesian information criterion

ngroupsNumber of groups in model

nobsNumber of observations included

norigNumber of observation in the original dataset

nexcludedNumber of excluded observations

convergedLogical - Did the model converge

estimatorEstimator used

missing_methodMethod 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

## Examples