Compares fit from one or several lavaan models. Also optionally includes references values. The reference fit values are based on Schreiber et al. (2006).
Arguments
- model
lavaan model object(s) to extract fit indices from
- model.labels
Model labels to use. If a named list is provided for
model, default to the names of the list. Otherwise, if the list is unnamed, defaults to generic numbering.- nice_table
Logical, whether to print the table as a
rempsyc::nice_tableas well as print the reference values at the bottom of the table.
Value
A dataframe, representing select fit indices (chi2, df, chi2/df, p-value of the chi2 test, CFI, TLI, RMSEA, SRMR, AIC, and BIC).
References
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of educational research, 99(6), 323-338. https://doi.org/10.3200/JOER.99.6.323-338
Examples
(latent <- list(
visual = paste0("x", 1:3),
textual = paste0("x", 4:6),
speed = paste0("x", 7:9)
))
#> $visual
#> [1] "x1" "x2" "x3"
#>
#> $textual
#> [1] "x4" "x5" "x6"
#>
#> $speed
#> [1] "x7" "x8" "x9"
#>
(regression <- list(
ageyr = c("visual", "textual", "speed"),
grade = c("visual", "textual", "speed")
))
#> $ageyr
#> [1] "visual" "textual" "speed"
#>
#> $grade
#> [1] "visual" "textual" "speed"
#>
HS.model <- write_lavaan(latent = latent, regression = regression)
cat(HS.model)
#> ##################################################
#> # [-----Latent variables (measurement model)-----]
#>
#> visual =~ x1 + x2 + x3
#> textual =~ x4 + x5 + x6
#> speed =~ x7 + x8 + x9
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> ageyr ~ visual + textual + speed
#> grade ~ visual + textual + speed
#>
library(lavaan)
fit <- sem(HS.model, data = HolzingerSwineford1939)
nice_fit(fit)
#> Model chi2 df chi2.df p CFI TLI RMSEA SRMR AIC BIC
#> 1 Model 1 116.263 36 3.23 0 0.926 0.887 0.086 0.06 8638.134 8749.248