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Extract relevant fit indices from lavaan model

Source: R/nice_fit.R
nice_fit.Rd

Compares fit from one or several lavaan models. Also optionally includes references values. The reference fit values are based on Schreiber (2017), Table 3.

Usage

nice_fit(
  model,
  model.labels,
  nice_table = FALSE,
  guidelines = TRUE,
  stars = FALSE,
  verbose = TRUE
)

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.

guidelines

Logical, if nice_table = TRUE, whether to display include reference values based on Schreiber (2017), Table 3, at the bottom of the table.

stars

Logical, if nice_table = TRUE, whether to display significance stars (defaults to FALSE).

verbose

Logical, whether to display messages and warnings.

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 (doi:10.1007/s11336-016-9552-7 ) in a noticeable way for smaller samples (thanks to James Uanhoro for this change).

If using guidelines = TRUE, please carefully consider the following 2023 quote from Terrence D. Jorgensen:

I do not recommend including cutoffs in the table, as doing so would perpetuate their misuse. Fit indices are not test statistics, and their suggested cutoffs are not critical values associated with known Type I error rates. Numerous simulation studies have shown how poorly cutoffs perform in model selection (e.g., , Jorgensen et al. (2018). Instead of test statistics, fit indices were designed to be measures of effect size (practical significance), which complement the chi-squared test of statistical significance. The range of RMSEA interpretations above is more reminiscent of the range of small/medium/large effect sizes proposed by Cohen for use in power analyses, which are as arbitrary as alpha levels, but at least they better respect the idea that (mis)fit is a matter of magnitude, not nearly so simple as "perfect or imperfect."

References

Schreiber, J. B. (2017). Update to core reporting practices in structural equation modeling. Research in social and administrative pharmacy, 13(3), 634-643. doi:10.1016/j.sapharm.2016.06.006

Examples

x <- paste0("x", 1:9)
(latent <- list(
  visual = x[1:3],
  textual = x[4:6],
  speed = 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