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

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