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, ...)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.level = 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.
A one-row tibble::tibble with columns:
Model chi squared
Number of parameters in the model
Root mean square error of approximation
95 percent upper bound on RMSEA
Standardised root mean residual
Adjusted goodness of fit
Comparative fit index
Tucker Lewis index
Akaike information criterion
Bayesian information criterion
Number of groups in model
Number of observations included
Number of observation in the original dataset
Number of excluded observations
Logical - Did the model converge
Estimator used
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
glance(), lavaan::cfa(), lavaan::sem(),
lavaan::fitmeasures()
Other lavaan tidiers:
tidy.lavaan()
# NOT RUN {
# }
# NOT RUN {
library(lavaan)
cfa.fit <- cfa(
"F =~ x1 + x2 + x3 + x4 + x5",
data = HolzingerSwineford1939, group = "school"
)
glance(cfa.fit)
# }
# NOT RUN {
# }
Run the code above in your browser using DataCamp Workspace