Extract relevant fit indices from lavaan model

Compares fit from one or several lavaan models. Also optionally includes references values. The reference fit values are based on Schreiber et al. (2006).

Usage

nice_fit(model, model.labels, nice_table = FALSE, stars = FALSE)

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_table as well as print the reference values at the bottom of the table.
stars: Logical, if nice_table = TRUE, whether to display significance stars (defaults to FALSE).

Value

A dataframe, representing select fit indices (chi2, df, chi2/df, p-value of the chi2 test, CFI, TLI, RMSEA and its 90% CI, unbiased SRMR, AIC, and BIC).

Details

Note that nice_fit reports the unbiased SRMR through lavaan::lavResiduals() because the standard SRMR is upwardly biased (https://doi.org/10.1007/s11336-016-9552-7) in a noticeable way for smaller samples (thanks to James Uanhoro for this change).

References

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

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   chisq df chi2.df pvalue   cfi   tli rmsea rmsea.ci.lower
#> 1 Model 1 116.263 36    3.23      0 0.926 0.887 0.086          0.069
#>   rmsea.ci.upper  srmr      aic      bic
#> 1          0.104 0.052 8638.134 8749.248