- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
Ordinal Logistic Regression: Overview, Implementation with Example
Updated on 03 July, 2023
7.17K+ views
• 9 min read
Table of Contents
In data science, working with variables is the most common thing as information is collected in the form of variables. The analysis of the variables is carried out to understand the processes of a business or research study. While working with the data, some tasks need to be performed to establish the relationship between the variables considered in the analysis. One such method that is widely used in understanding the behavior of the variables in regression analysis. Linear and logistic regression are the two types of regression analysis that have often been applied in most studies. However, the knowledge of regression remains limited.
With the different types of variables used in the study, the type of regression method changes too. There is this capability of dealing with different types of variables. The multi-level dependent variables can be analyzed through the use of regression analysis. However, to perform the analysis over such variables, specialized computational techniques are available. Several algorithms of machine learning such as random forest, decision tree, and Naive Bayes. The algorithms are a bit complicated initially, but if the logistic regression technique is understood well, then grasping the working of the algorithms is an easy process. The article focuses on the topic of ordinal regression.
The parameters that affect the degree of nodal involvement in patients with oral cancer are described using an ordinal logistic regression model, and its future validation is discussed.
Ordinal Regression
It is used for predicting the value of an ordinal dependent variable when there is the presence of one independent variable or more than one independent variable. An ordinal variable can be defined as a variable that has a value on an arbitrary scale. The ordinary regression technique is often considered as a technique between the techniques of classification and regression.
The technique of ordinal regression is also known as ordinal logistic regression. It is mostly an extension of the technique of binomial logistic regression. An ordinary regression technique performs to predict the dependent variable with multiple ordered categories and independent variables. However, it can also be explained as a technique that facilitates the interaction between independent and dependent variables. To understand the concept more clearly, let us consider an example.
Assuming that a survey has been conducted, the respondents were asked whether they agreed or disagreed. However, the responses that were generated didn’t help in the study well. Therefore, further categories of responses were generated, such as disagree, strongly agree, strongly disagree, or agree. Once the categorizations were done in an ordered manner, it helped understand the nature of the responses. This is what is captured by the technique of ordinal logistic regression, where the categories are formed based on certain orders.
After carrying out the technique of ordinal regression, the user will be able to predict which independent variables are statistically significant to the dependent variable. For all the categorical independent variables, the user will predict the odds of which one group has a lower or higher value on the dependent variables. Also, for predicting the increase or decrease in the variables by a single unit, the user can use the OLR method.
The method can be widely used in several domains of studies. Because of this advantage of application in a wide range of studies, the model is the most admired in data analytics. Sometimes, the method is also referred to as the model of proportional odds.
Machine learning techniques can be used for carrying out the techniques of ordinary regression. It is also called ranking learning in machine learning. The technique is often performed through the model of generalized linear model (GLM). Various software provides the provision of carrying out the regression analysis. Such software’s are ORCA, MATLAB framework, and R packages such as Ordinal and MASS.
Statistical Models in Ordinary Logistic Regression
To handle the outcomes in the ordinal form, several models of ordinal logistic regression are present. Every model is different and has different ways of forming the logistics. Examples of such models are the proportional odds, continuation ratio, and adjacent category models. Every model that is used in the OLR studies has its limitations as well as advantages. As per the needs, the users can choose the models. The models of adjacent categories and the continuation ratio do not rely on the complete data. Also, in the applications such as biomedical and epidemiological studies, the model of proportional odds is often used. However, there might be cases where the user can also observe the application of the continuation ratio model. Also, it depends on what purposes the statistical analysis is to be carried out.
Assumptions to be Made in the OLR
For carrying out the OLR studies in the SPSS software, a few assumptions are required to be considered. The assumptions are listed below:
- The measurement of the dependent variable should be done at the ordinal level.
- The independent variable should be one or more in number. They should be continuous, categorical, or ordinal, which also includes the dichotomous variables.
- There should not be any multicollinearity between the independent variables. If there is any high correlation between any independent variables, then it creates the case of multicollinearity.
- The model should have proportional odds.
Ordinary Logistic Regression (OLR) in R
The following libraries are required in order to perform the OLR in R:
> require(ggplot2)
> require(foreign)
> require(Hmisc)
> require(MASS)
> require(reshape2)
1. Loading the data: Once the libraries are loaded, the data then needs to be loaded.
2. Understanding the data: A variable “apply” is present in the dataset, acting as the dependent variable. There are three levels in the variable: very likely, somewhat likely, and unlikely, with the “very likely” is the highest while the “unlikely” is the lowest. It can be seen that there are ordered categories present in the data. Therefore, in such situations, ordinary logistic regression can be applied. If there is a pairing of (o/1), then it refers to a graduate degree with at least one parent and the public (0/1) refers to the institute type.
The command “polr” is used for building the model of ordinary logistic regression. The Hess=TRUE is then specified to show the model’s output as the information matrix retrieved from the optimization. This is done to receive any standard errors associated with the model.
The output shows the usual table of the output coefficient of the regression that includes the value for the standard errors of each coefficient. It also includes the values, residual deviance, estimated value for the intercepts, and the value for the AIC. The criteria for the information are AIC. If the value of AIC is lesser, it indicates a better model.
The next calculation is done for the metrics such as the Odds ratio, Cl, and the p-Value.
Interpretation of the Output
You can interpret the output generated from the Ordinary Regression in the following manner:
- There has been an increase of one unit in the section of parental education, from the value of 0 to 1, i.e., from the low to high. The odds for the variable, i.e., “very likely” to “somewhat likely” or “unlikely,” are combined to form a value of 2.85 or greater.
- In case there is a movement of 1 unit in the student’s GPA, the odds of “unlikely” to “somewhat likely” or “very likely” is multiplied by 1.85.
The model is then enhanced for better prediction. Finally, interaction terms are added to the model. Once these things are done, it is then time to plot the model.
Ordinary Logistic Regression Examples
There are several examples where the ordinary logistic regression technique can be applied. A few examples are listed below.
- Suppose a marketing firm investigates the factors that influence the soda size ordered by people in most fast food outlets. The sizes can be small, medium, extra-large, or large, depending on the requirements. Several factors might lead to ordering the sodas, such as whether the customer has ordered a sandwich or some French fries. It also depends on the age of the customer.
- Studies need to be conducted to analyze the factors that might influence the medalling in the swimming category in Olympics. The factors in these cases might be the hours of practice, the age of the swimmer, and the diet. It might also depend on how popular swimming is in the home country of the swimmer.
Join Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
Methods
The Department of Surgical Oncology, Dr. BRA-IRCH, AIIMS, New Delhi, India, provided the data used to develop the models. From 1995 through 2013, all OSCC patients who underwent complete surgery, including neck dissection, were included. For the model’s validation, additional data from 204 patients gathered prospectively between 2014 and 2015 were taken into account.
As a pioneering effort in the field of OSCC, a stepwise multivariable regression approach was utilised to evaluate the factors connected to the degree of nodal involvement. The results are shown as odds ratios and the matching 95% confidence interval (CI). The ordinal models were evaluated and compared for proper ordinal form accounting. Additionally, a prospectively acquired set of additional data was used to validate the established model’s performance.
Results
Pain at the time of presentation, sub mucous fibrosis, a palpable neck node, oral site, and degree of differentiation were discovered to be strongly linked variables with the extent of nodal involvement under a multivariable proportional odds model. In addition, the partial-proportional odds model revealed that tumour size was also relevant.
Here are some examples of ordinal logistic regression.
Examples of ordinal logistic regression
Example 1: A marketing research company is looking at the variables that affect the soda size that customers order at a fast food restaurant (small, medium, large, or extra large). These elements could include the sandwich purchased (chicken or burger), whether or not fries were also requested, and the customer’s age. Although the outcome variable, soda size, is explicitly ordered, there are inconsistent differences between the different sizes. Small and medium are separated by 10 ounces, medium and large by 8, and extra large by 12 ounces.
Example 2: A researcher is curious about the elements that affect Olympic swimming medaling. Hours of training, food, age, and the popularity of swimming in the athlete’s own nation are all pertinent factors. According to the researcher, bronze and silver are separated from each other by a greater distance.
Example 3: A study examines the variables that affect applicants’ choices for graduate programs. Students in their junior year of college are asked if they are extremely likely, slightly likely, or unlikely to apply to graduate school. Thus, there are three types in our outcome variable. Data on the level of education of the parents, the nature of the undergraduate institution (public vs. private), and the current GPA are also gathered. The “distances” between these three points may not be comparable, according to the researchers. The “distance” between “unlikely” and “somewhat likely,” for instance, can be less than that between “somewhat likely” and “very likely.”
Conclusion
The statistical technique of ordinary regression and how to implement it in R have been discussed in this article. The technique is considered an extensor for the simple logistics model where categorical dependent variables are used. It returns the information of contribution from each of the independent variables. The benefit of the OLR over the multinomial regression model is that the information of the dependent variable’s ranking is not preserved when the contribution information is shown for all the independent variables. Also, in the case of OLR, every variable can be normalized as all the variables have a different scale.
Enroll for Advanced Certification in Master of Science in Machine Learning & AI.
RELATED PROGRAMS