Linear Regression in R – principal component regression in R

Hits: 45

Linear Regression in R – principal component regression in R

Linear regression is a statistical method used to understand the relationship between a dependent variable (also known as the outcome or response variable) and one or more independent variables (also known as predictors or explanatory variables). In other words, it is used to predict the value of a dependent variable based on the values of one or more independent variables.

Principal component regression (PCR) is a variation of linear regression that is used when there are a large number of independent variables. PCR works by reducing the number of independent variables by transforming them into a smaller set of uncorrelated variables called principal components. These principal components are linear combinations of the original independent variables, and they are chosen in such a way that they explain as much of the variance in the data as possible.

Using PCR in R is relatively easy. First, you would need to install the “pls” package in R. Then, you can use the “pcr” function in the package to fit a PCR model to your data. The function takes two main arguments: the independent variables and the dependent variable. You can also specify the number of principal components to use in the model.

Once the model is fit, you can use the “predict” function to make predictions on new data. Additionally, you can use the “summary” function to get a summary of the model’s performance, including information about the R-squared value, which is a measure of how well the model fits the data.

In summary, PCR is a variation of linear regression that is used when there are a large number of independent variables. It works by reducing the number of independent variables by transforming them into a smaller set of uncorrelated variables called principal components. It is relatively easy to implement in R using the “pls” package and the “pcr” function.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Linear Regression in R – principal component regression in R.



Linear Regression in R – principal component regression in R

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!