Prediction in Various Logistic Regression Models (Part 2)

Statistics in R Series

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Introduction

We have covered logistic regression models for both binary and ordinal data and also demonstrated how to implement the model in R. Moreover the prediction analysis using the R libraries was also discussed in earlier articles. We have seen the impact of single as well as multiple predictors on the response variable and quantified it. Binary and ordinal response variables were taken to show how to deal with different types of data. In this article, we will go through four more prediction analyses for logistic regression models namely Generalized Ordinal Regression model, Partial Proportional Odd model, Multinomial Logistic model and Poisson Regression model.

Dataset

Our research will use the same UCI Machine Learning Repository’s Adult Data Set as a case study. More than 30000 individuals’ demographic data are collected in this dataset. Data include each individual’s race, education, job, gender, salary, number of jobs held, hours worked per week, and income earned. To get a refresher, the variables under consideration are shown below.

• Education: numeric and continuous. The health status of an individual can be greatly affected by education.
• Marital status: binary (0 for…