# modeldiag [](https://github.com/Teniola17/modeldiag/actions) [](https://CRAN.R-project.org/package=modeldiag) The goal of modeldiag is to provide comprehensive diagnostic checks for statistical models including linear models, generalized linear models, and survival models. ## Installation You can install the development version of modeldiag from [GitHub](https://github.com/) with: ``` r # install.packages("devtools") devtools::install_github("Teniola17/modeldiag") ``` ## Example This is a basic example which shows you how to diagnose a linear model: ```r library(modeldiag) # Fit a linear model model <- lm(mpg ~ wt + hp + disp, data = mtcars) # Run diagnostics diagnostics <- diagnose_model(model) # View summary summary(diagnostics) # Plot diagnostics plot(diagnostics) ``` ## Supported Models The package currently supports: - **Linear models** (`lm`): Tests for multicollinearity, heteroscedasticity, autocorrelation, normality, and outliers - **Generalized linear models** (`glm`): - Binomial family: Tests for linearity of logit, goodness of fit, influential observations, and separation - Poisson family: Tests for overdispersion, zero-inflation, and residual analysis - **Cox proportional hazards models** (`coxph`): Tests for proportional hazards assumption, influential observations, and functional form ## Features ### Diagnostic Tests Each model type has specific diagnostic tests: #### Linear Models - Variance Inflation Factor (VIF) for multicollinearity - Breusch-Pagan test for heteroscedasticity - Durbin-Watson test for autocorrelation - Shapiro-Wilk test for normality of residuals - Cook's distance for influential observations #### Logistic Regression - VIF for multicollinearity - Box-Tidwell test for linearity of logit - Hosmer-Lemeshow test for goodness of fit - Complete/quasi-complete separation detection - Cook's distance for influential observations #### Poisson Regression - VIF for multicollinearity - Overdispersion test - Zero-inflation test - Residual analysis - Cook's distance for influential observations #### Cox Models - VIF for multicollinearity (with warnings) - Schoenfeld residuals test for proportional hazards - dfbetas for influential observations - Guidance for functional form assessment ### Visualization The `plot()` method provides model-specific diagnostic plots: - Residuals vs Fitted values - Q-Q plots - Scale-Location plots - Cook's distance plots - ACF plots for time series residuals (linear models) - Schoenfeld residual plots (Cox models) - dfbeta plots (Cox models) - Martingale residual plots (Cox models) ## Getting Help If you encounter a bug, please file an issue with a minimal reproducible example on [GitHub](https://github.com/Teniola17/modeldiag/issues).# modeldiag