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Evaluate model fit of an estimated causal graph

Source: R/evaluate_model_fit.r
evaluate_model_fit.Rd

Fits the causal graph implied by adjacency_matrix as a structural equation model (SEM) via lavaan::sem() and returns standard SEM fit measures (CFI, RMSEA, AIC/BIC, etc.). This is an R port of the Python lingam.utils.evaluate_model_fit(), which delegates to the Python package semopy; this R version delegates to lavaan instead.

Usage

evaluate_model_fit(adjacency_matrix, X, is_ordinal = NULL)

Arguments

adjacency_matrix

p x p numeric adjacency matrix (NA allowed for latent confounder pairs), or a lingamr result object (e.g. LingamResult, ParceLingamResult, LiMResult) with an adjacency_matrix element, from which the matrix is extracted automatically

X

numeric matrix or data frame (n_samples x p) with no missing values

is_ordinal

logical or 0/1 vector of length p. TRUE marks a variable as ordinal (categorical), fit with lavaan's WLSMV-based estimator. NULL (default) treats all variables as continuous.

Value

A one-row data.frame of fit measures: DoF, DoF Baseline, chi2, chi2 p-value, chi2 Baseline, CFI, GFI, AGFI, NFI, TLI, RMSEA, AIC, BIC, LogLik. When is_ordinal is used, AIC/BIC/LogLik and some other measures are not defined by the WLSMV estimator and are returned as NA.

Details

  • Optional dependency: this function requires the lavaan package (listed in Suggests, not Imports). Install it with install.packages("lavaan").

  • Latent confounders: an NA element B[i, j] (as produced by e.g. lingam_parce() for a suspected latent confounder between variables i and j) is represented as a residual covariance xi ~~ xj in the lavaan model. This is algebraically equivalent to the two-indicator latent common cause (one loading fixed to 1) used by the Python semopy implementation, but is expressed with lavaan's standard residual-covariance idiom rather than an explicit latent variable.

  • Numerical values will not match semopy exactly: lavaan and semopy use different default estimators/options. The fit measures returned are the same statistics, but exact numbers can differ.

  • Convention: adjacency_matrix follows the lingamr convention B[i, j] = causal coefficient from variable j to variable i (j -> i).

References

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. doi:10.18637/jss.v048.i02

Examples

if (requireNamespace("lavaan", quietly = TRUE)) {
  dat <- generate_lingam_sample_6()
  result <- lingam_direct(dat$data, reg_method = "ols")
  evaluate_model_fit(result, dat$data)
}
#>   DoF DoF Baseline chi2 chi2 p-value chi2 Baseline CFI GFI AGFI NFI TLI RMSEA
#> 1   0           15    0           NA       23023.7   1   1    1   1   1     0
#>        AIC      BIC    LogLik
#> 1 1860.598 1958.753 -910.2991