In this post you will discover 4 recipes for linear regression for the R platform.

You can copy and paste the recipes in this post to make a jump-start on your own problem or to learn and practice with linear regression in R.

Each example in this post uses the longley dataset provided in the datasets package that comes with R. The longley dataset describes 7 economic variables observed from 1947 to 1962 used to predict the number of people employed yearly.

## Ordinary Least Squares Regression

Ordinary Least Squares (OLS) regression is a linear model that seeks to find a set of coefficients for a line/hyper-plane that minimise the sum of the squared errors.

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# load data data(longley) # fit model fit <- lm(Employed~., longley) # summarize the fit summary(fit) # make predictions predictions <- predict(fit, longley) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |

Learn more about the **lm** function and the stats package.

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## Stepwize Linear Regression

Stepwise Linear Regression is a method that makes use of linear regression to discover which subset of attributes in the dataset result in the best performing model. It is step-wise because each iteration of the method makes a change to the set of attributes and creates a model to evaluate the performance of the set.

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# load data data(longley) # fit model base <- lm(Employed~., longley) # summarize the fit summary(base) # perform step-wise feature selection fit <- step(base) # summarize the selected model summary(fit) # make predictions predictions <- predict(fit, longley) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |

Learn more about the **step** function and the stats package.

## Principal Component Regression

Principal Component Regression (PCR) creates a linear regression model using the outputs of a Principal Component Analysis (PCA) to estimate the coefficients of the model. PCR is useful when the data has highly correlated predictors.

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# load the package library(pls) # load data data(longley) # fit model fit <- pcr(Employed~., data=longley, validation="CV") # summarize the fit summary(fit) # make predictions predictions <- predict(fit, longley, ncomp=6) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |

Learn more about the **pcr** function and the pls package.

## Partial Least Squares Regression

Partial Least Squares (PLS) Regression creates a linear model of the data in a transformed projection of problem space. Like PCR, PLS is appropriate for data with highly-correlated predictors.

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# load the package library(pls) # load data data(longley) # fit model fit <- plsr(Employed~., data=longley, validation="CV") # summarize the fit summary(fit) # make predictions predictions <- predict(fit, longley, ncomp=6) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |

Learn more about the **plsr** function and the pls package.

## Summary

In this post you discovered 4 recipes for creating linear regression models in R and making predictions using those models.

Chapter 6 of Applied Predictive Modeling by Kuhn and Johnson provides an excellent introduction to linear regression with R for beginners. Practical Regression and Anova using R (PDF) by Faraway provides a more in-depth treatment.

Is there any reason why SLR is not performing better than OLSR? again, the question comes how to judge ‘better’. I notice that rmse(SLR)>rmse(OLSR). If we are aiming for a better fit shouldn’t the rmse decrease?

I think the rmse calculation step missed taking the square root.

rmse <- sqrt( mean((longley$Employed – predictions)^2) )

Yes thank you, this was something I noticed as well and was unsure of.

Jason calculated mse not rmse. So there is no square root.