Linear Regression in R – partial least squares regression in R

Linear Regression is a statistical method used to understand the relationship between a dependent variable and one or more independent variables. However, when the number of independent variables is large, linear regression can become difficult to interpret and may not be able to accurately model the data. In these cases, a variation of linear regression called partial least squares (PLS) regression may be used.

PLS regression is a technique that reduces the number of independent variables while still capturing the important information in the data. It does this by creating new variables, called latent variables, that are linear combinations of the original independent variables. These latent variables are then used in the linear regression model instead of the original independent variables.

In R, the pls package provides the plsr() function for performing PLS regression. Similar to lm() function, it takes a formula specifying the dependent and independent variables, and a dataset as its arguments and returns a PLS model object. You can use the summary() function to check the model’s accuracy, and plot() function to visualize the model.

It’s important to note that PLS regression assumes that the independent variables are correlated and also requires scaling of the data. It’s also worth noting that PLS regression can be useful for interpreting complex data sets, but it can be more prone to overfitting than OLS regression.

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 – partial least squares regression in R.

Linear Regression in R – partial least squares regression in R

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