The goal of psymetrics is to provide tools for extracting and visualizing psychometric model fit indices. It is compatible with models created using packages like lavaan, psych, and mirt.
You can install the development version of psymetrics from GitHub with:
# install.packages("pak")
pak::pak("brianmsm/[email protected]")Here is an example of how to use the psymetrics package with a model created using lavaan.
library(psymetrics)
library(lavaan)
#> This is lavaan 0.6-19
#> lavaan is FREE software! Please report any bugs.
# Define a simple CFA model
model <- 'visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9'
# Fit the model using lavaan
fit <- cfa(model, data = HolzingerSwineford1939, estimator = "MLR")
# Extract and print fit indices
model_fit(fit)
#> NOBS | ESTIMATOR | NPAR | Chi2(24) | p (Chi2) | CFI | TLI | RMSEA | RMSEA CI | SRMR
#> ----------------------------------------------------------------------------------------------
#> 301 | MLR | 21 | 87.132 | < .001 | 0.925 | 0.888 | 0.093 | [0.073, 0.115] | 0.065
# You can also request specific types of indices, such as 'robust'
model_fit(fit, type = "robust")
#> NOBS | ESTIMATOR | NPAR | Chi2(24) | p (Chi2) | CFI | TLI | RMSEA | RMSEA CI | SRMR
#> ----------------------------------------------------------------------------------------------
#> 301 | MLR | 21 | 87.132 | < .001 | 0.930 | 0.895 | 0.092 | [0.072, 0.114] | 0.065
# Or specify which indices to extract
model_fit(fit, metrics = c("cfi", "tli"))
#> cfi and tli were adjusted to their scaled version.
#> If you want to control the specific metric type used, specify it explicitly
#> (e.g., `cfi.robust`) or modify the type argument.
#> NOBS | ESTIMATOR | NPAR | CFI | TLI
#> ---------------------------------------
#> 301 | MLR | 21 | 0.925 | 0.888This example demonstrates how to extract and print various fit indices from a confirmatory factor analysis (CFA) model using psymetrics. You can choose between standard, scaled, or robust fit indices, and even specify custom sets of indices to extract.
fit_1 <- cfa(model, data = HolzingerSwineford1939, estimator = "MLR")
fit_2 <- cfa(model, data = HolzingerSwineford1939, estimator = "ULSM")
fit_table <- compare_model_fit(fit_1, fit_2)
fit_table
#> MODEL | NOBS | ESTIMATOR | NPAR | Chi2(24) | p (Chi2) | CFI | TLI | RMSEA | RMSEA CI | SRMR
#> ------------------------------------------------------------------------------------------------------
#> fit_1 | 301 | MLR | 21 | 87.132 | < .001 | 0.925 | 0.888 | 0.093 | [0.073, 0.115] | 0.065
#> fit_2 | 301 | ULSM | 21 | 90.600 | < .001 | 0.931 | 0.897 | 0.096 | [0.073, 0.120] | 0.059In this example, compare_model_fit is used to compare the fit indices of two different models. This function allows you to easily see the differences in model fit across different estimation methods or model specifications.
This is useful when you want to embed the output directly in HTML reports or web pages.
print(fit_table, format = "html")| MODEL | NOBS | ESTIMATOR | NPAR | Chi2(24) | p (Chi2) | CFI | TLI | RMSEA | RMSEA CI | SRMR |
|---|---|---|---|---|---|---|---|---|---|---|
| fit_1 | 301 | MLR | 21 | 87.132 | < .001 | 0.925 | 0.888 | 0.093 | [0.073, 0.115] | 0.065 |
| fit_2 | 301 | ULSM | 21 | 90.600 | < .001 | 0.931 | 0.897 | 0.096 | [0.073, 0.120] | 0.059 |
This is ideal for including the output in Markdown documents, such as GitHub READMEs or R Markdown reports.
print(fit_table, format = "markdown")#> |MODEL | NOBS | ESTIMATOR | NPAR | Chi2(24) | p (Chi2) | CFI | TLI | RMSEA | RMSEA CI | SRMR |
#> |:-----|:----:|:---------:|:----:|:--------:|:--------:|:-----:|:-----:|:-----:|:--------------:|:-----:|
#> |fit_1 | 301 | MLR | 21 | 87.132 | < .001 | 0.925 | 0.888 | 0.093 | [0.073, 0.115] | 0.065 |
#> |fit_2 | 301 | ULSM | 21 | 90.600 | < .001 | 0.931 | 0.897 | 0.096 | [0.073, 0.120] | 0.059 |
The save_table() function allows you to export the fit indices to a
Word document (.docx) with APA-style formatting and optional templates
for vertical or landscape orientation.
# Save the fit comparison table to Word
save_table(fit_table, path = "model_fit.docx", orientation = "landscape")The exported document will have a clean and professional format that you can directly include in reports or presentations.
You can visualize the factor loadings of your model with the
plot_factor_loadings() function. This function creates a dot plot of
standardized factor loadings, with the option to display confidence
intervals for each loading.
plot_factor_loadings(fit)
In this example, plot_factor_loadings() displays the factor loadings for each item on the respective factors, with confidence intervals. The plot can be adjusted to automatically scale the x-axis or group items by factor.