Ecevit Eyduran's ehaGoF calculates 15 different goodness of fit criteria for statistical models. The metrics include standard deviation ratio, coefficient of determination, Akaike's information criterion, and root mean square error. The dataset's specific size, format, and temporal coverage are not detailed in the provided metadata.
Use Cases
- Compare model performance based on the 15 listed goodness of fit criteria.
- Select the best model from a candidate set using information criteria like AIC and BIC.
- Assess prediction error magnitude using metrics such as RMSE, MSE, and MAPE.
- Evaluate the proportion of variance explained by a model using R-squared and adjusted R-squared.
Strengths
- Calculates 15 distinct goodness of fit metrics, providing a multi-faceted evaluation.
- Includes both common metrics like R-squared and specialized ones like the performance index (PI).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Ecevit Eyduran