Diagnostics through Residual Plots in Binomial Regression Addressing Chemical Species Data

Abstract

Binomial regression is used as a generalized linear model (GLM) in natural sciences to identify the covariate structure that is responsible for outcomes. It is very important to assess the adequacy and effectiveness of any model before its implementation. In GLM context, this study explores the structure and usefulness of partial residual (PRES), augmented partial residual (APRES), and conditional expectation and residuals (CERES) plots for visualizing influence diagnostics as a function of selected predictors. Binomial regression is considered here with predictor transformation, and PRES, APRES, and CERES plots are constructed for diagnostics of outliers and multicollinearity. The efficacy of these plots for obtaining a good visual impression may be varied due to behaviour of response variable and allied link function with different covariates. The certain techniques are applied on the data of hindered internal rotational (HIR) treatment of chemical species to recognize patterns for efficient modelling. The power of the tests for different plots shows that APRES and CERES (L) endure maximum power for detection of outliers and multicollinearity. The results revealed that residuals plots are more effective as compared to the conventional methods and help the scientists to easily and effectively model the data for their diagnostics policies.