AI & Machine Learning calculator

SRMR and RMSEA fit calculator

Compute Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA) model fit indices.

Run this test live in StatLab npm i @statlab/core

When to use it

Use for evaluating overall fit of structural equation models or covariance structures.

Required Inputs

  • Observed correlation matrix
  • Model-implied correlation matrix
  • Degrees of freedom

Mathematical Formula

SRMR = √[ (2 / (p(p+1))) Σ Σ (r_ij - σ̂_ij)² ]

Reporting Cautions

  • SRMR < 0.08 and RMSEA < 0.06 indicate good model fit.
  • Sensitive to sample size and model complexity.

Code Snippets (Python, R, TypeScript)

Python (SciPy / Statsmodels) 
import semopy
# Compute SRMR and RMSEA model fit metrics
R Language 
library(lavaan)
fitMeasures(fit_obj, c("srmr", "rmsea"))
TypeScript (@statlab/core) 
import { semFit } from '@statlab/core';
const res = semFit(obs, exp, df);

Developer Use Cases & Production Integrations

  • Evaluating structural model fit quality for complex telemetry graph relationships.
  • Validating factor structure fit in VoxelPulse analytics.

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