biostatistics-workbench

To fit a GLM or GLMM to ecological abundance data, the workflow fits the model and then runs assumption diagnostics for normality, homoscedasticity, and residual checks. It validates the model and reports effect sizes with confidence intervals.

The best way to compare competing ecological models is by applying model selection with AIC, AICc, and BIC. The workflow evaluates alternative models and supports model averaging to identify the strongest ecological relationships.

To calculate effect sizes and marginal R-squared for mixed models in ecology, the workflow estimates effect sizes with confidence intervals and computes marginal and conditional R-squared values. This quantifies the variance explained by fixed and random effects.

Yes, you can use numpy and pandas datasets for ecological hypothesis testing and regression analysis. The workflow relies on these dependencies to process data, perform classical tests, and execute regression analysis across ecological datasets.

You should use GLMMs instead of classical tests for ecological occupancy data when dealing with hierarchical structure or random effects. The workflow selects appropriate statistical methods, fitting mixed models to account for nested ecological data.