I have to run many CFAs and want to automate saving specific output values in a data frame so I can convert it to a latex table later.
Specifically I get my output something like this using lavaan:
model <- 'y =~ x1 x2 x3'
fit <- cfa(model, data)
sum <- summary(fit_os_bi, fit.measures=TRUE, standardized=T)
I managed to extract some values like this p_val <- sum$test$standard$pvalue but I couldn't figure out how to get to CFI, TLI, RMSEA, and SRMR. I think I'm even missing the right search terms to google that problem successfully.
How can I access these values from the summary-object? I'd be grateful if you could provide me with the right code or point me to a resource that explains it!
Here's an excerpt of the cfa summary:
lavaan 0.6-12 ended normally after 42 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 9
Number of observations 213
Model Test User Model:
Test statistic 1.625
Degrees of freedom 1
P-value (Chi-square) 0.202
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.983
Root Mean Square Error of Approximation:
RMSEA 0.054
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.200
P-value RMSEA <= 0.05 0.315
Standardized Root Mean Square Residual:
SRMR 0.014
I looked at 'sum' in the environment inspector in R-Studio (where I found the location of the p-value) and searched the documentation of lavaan, but to no avail.
Since the values I'm looking for appear in the output I expect they must be stored somewhere in the summary-object.
CodePudding user response：
lavaan has a number of helper functions to extract coefficients from the model object. In this case you can use fitMeasures():
fitMeasures(fit, c("pvalue", "cfi", "tli", "rmsea","srmr"))
CodePudding user response：
Use broom::tidy() and broom::glance() to get model information in a data.frame object. I will use the standard example from lavaan package
library(lavaan)
#> This is lavaan 0.6-12
#> lavaan is FREE software! Please report any bugs.
HS.model <- ' visual =~ x1 x2 x3
textual =~ x4 x5 x6
speed =~ x7 x8 x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
broom::tidy(fit)
#> # A tibble: 24 × 9
#> term op estimate std.e…¹ stati…² p.value std.lv std.all std.nox
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 visual =~ x1 =~ 1 0 NA NA 0.900 0.772 0.772
#> 2 visual =~ x2 =~ 0.554 0.0997 5.55 2.80e- 8 0.498 0.424 0.424
#> 3 visual =~ x3 =~ 0.729 0.109 6.68 2.31e-11 0.656 0.581 0.581
#> 4 textual =~ x4 =~ 1 0 NA NA 0.990 0.852 0.852
#> 5 textual =~ x5 =~ 1.11 0.0654 17.0 0 1.10 0.855 0.855
#> 6 textual =~ x6 =~ 0.926 0.0554 16.7 0 0.917 0.838 0.838
#> 7 speed =~ x7 =~ 1 0 NA NA 0.619 0.570 0.570
#> 8 speed =~ x8 =~ 1.18 0.165 7.15 8.56e-13 0.731 0.723 0.723
#> 9 speed =~ x9 =~ 1.08 0.151 7.15 8.40e-13 0.670 0.665 0.665
#> 10 x1 ~~ x1 ~~ 0.549 0.114 4.83 1.34e- 6 0.549 0.404 0.404
#> # … with 14 more rows, and abbreviated variable names ¹std.error, ²statistic
broom::glance(fit)
#> # A tibble: 1 × 17
#> agfi AIC BIC cfi chisq npar rmsea rmsea.conf.h…¹ srmr tli conve…²
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 0.894 7517. 7595. 0.931 85.3 21 0.0921 0.114 0.0652 0.896 TRUE
#> # … with 6 more variables: estimator <chr>, ngroups <int>,
#> # missing_method <chr>, nobs <int>, norig <int>, nexcluded <int>, and
#> # abbreviated variable names ¹rmsea.conf.high, ²converged
Created on 2023-01-26 with reprex v2.0.2