glance.lavaan {broom} | R Documentation |
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 modelling function. This includes the name of the modelling function or any arguments passed to the modelling 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
.
## S3 method for class 'lavaan' glance(x, ...)
x |
A |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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
glance()
, lavaan::cfa()
, lavaan::sem()
,
lavaan::fitmeasures()
Other lavaan tidiers:
tidy.lavaan()
if (require("lavaan", quietly = TRUE)) { library(lavaan) cfa.fit <- cfa( 'F =~ x1 + x2 + x3 + x4 + x5', data = HolzingerSwineford1939, group = "school" ) glance(cfa.fit) }