Extract model fit — k_model

Model fit indices extracted from k-folds

Usage

k_model_fit(models, index = "default", by.fold = TRUE)

Arguments

models: an object returned from kfa
index: character; one or more fit indices to summarize in the report. Use index_available to see choices. Chi-square value and degrees of freedom are always reported. Default is CFI and RMSEA (naive, scaled, or robust version depends on estimator used in models).
by.fold: Should each element in the returned lists be a fold (default) or a factor model?

Value

list of data.frames with average model fit for each factor model

Examples

data(example.kfa)

# customize fit indices to report
k_model_fit(example.kfa, index = c("chisq", "cfi", "rmsea", "srmr"))
#> [[1]]
#>      model    chisq  df       cfi     rmsea       srmr
#> 1 1-factor 1842.728 170 0.3982157 0.1477066 0.15871353
#> 2 2-factor 1179.387 169 0.6365010 0.1151363 0.13286334
#> 3 3-factor  154.693 167 1.0000000 0.0000000 0.02918932
#> 
#> [[2]]
#>      model     chisq  df       cfi      rmsea       srmr
#> 1 1-factor 1635.0572 170 0.4142103 0.13854143 0.15298996
#> 2 3-factor  184.2802 167 0.9930907 0.01518072 0.03465284
#> 

# organize results by factor model rather than by fold
k_model_fit(example.kfa, by.fold = FALSE)
#> $`1-factor`
#>   fold chisq.scaled df.scaled cfi.scaled rmsea.scaled
#> 1    1     489.3249  46.35648 0.07403330    0.1455603
#> 4    2     530.2678  56.27182 0.09795945    0.1369679
#> 
#> $`2-factor`
#>   fold chisq.scaled df.scaled cfi.scaled rmsea.scaled
#> 2    1      401.284  58.37055  0.2831848    0.1141319
#> 
#> $`3-factor`
#>   fold chisq.scaled df.scaled cfi.scaled rmsea.scaled
#> 3    1     103.0409  110.7621  1.0000000   0.00000000
#> 5    2     118.7127  108.3270  0.9802355   0.01461252
#>